Tech on the Rocks Transistor (https://transistor.fm) https://feeds.transistor.fm/tech-on-the-rocks Join Kostas and Nitay as they speak with amazingly smart people who are building the next generation of technology, from hardware to cloud compute. Tech on the Rocks is for people who are curious about the foundations of the tech industry. Recorded primarily from our offices and homes, but one day we hope to record in a bar somewhere. Cheers! © 2026 Kostas, Nitay e6b3604e-5537-5560-80a9-865a80fc693c no en Sat, 16 May 2026 21:25:36 -0700 Sat, 16 May 2026 21:26:01 -0700 https://img.transistorcdn.com/4vyBawFJq7hOmJ47YkMrv3pnePxdyNY-qdJuaMK9tRs/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS80ZWI0/YjY4MDc1YmU2MDI0/MTNkNjYwZWYwYzky/ZGEyNi5qcGc.jpg Tech on the Rocks episodic Kostas, Nitay Join Kostas and Nitay as they speak with amazingly smart people who are building the next generation of technology, from hardware to cloud compute. Tech on the Rocks is for people who are curious about the foundations of the tech industry. Recorded primarily from our offices and homes, but one day we hope to record in a bar somewhere. Cheers! Join Kostas and Nitay as they speak with amazingly smart people who are building the next generation of technology, from hardware to cloud compute. technology, infrastructure, cloud, systems, data Kostas Pardalis, Nitay Joffe hosts@techontherocks.show No No From Session Replays to Autonomous Improvement: Shipping the First AI Product Engineer with Milana 26 26 From Session Replays to Autonomous Improvement: Shipping the First AI Product Engineer with Milana full 81f675e3-a23d-41b2-8cc3-da0295bec764 https://techontherocks.show/26 In this episode, we sit down with Rohan Katyal and Raghav Sethi, co-founders of Milana, to discuss the shift from passive analytics to the world’s first AI Product Engineer. Rather than just providing another dashboard to monitor, Rohan and Raghav are building an agentic partner that you add to your product to bridge the gap between discovery and deployment. Drawing on their experience at Meta, Yelp, and Airtable, they explore how Milana enables autonomous improvement - turning deep user intelligence into shippable code and structural refinements that act as a tireless extension of your engineering team.

The conversation dives into why session replays — a mature but historically underused technology — are now a powerful data asset thanks to vision LLMs. Raghav explains how session replays are really just high-granularity logging of DOM changes, not screen recordings, and why feeding them through AI unlocks insights that traditional event-based analytics simply can’t capture. The team breaks down how they use just-in-time structuring to extract meaning from dense, unstructured session data without requiring upfront instrumentation.

Rohan shares hard-won lessons from building Yelp’s experimentation platform — including how teams that simply ran more experiments consistently outperformed those with better data resources. They discuss the tension between A/B testing rigor and iteration speed, why most experiments never ship, and how lowering the cost of generating and testing hypotheses changes everything about product development velocity.

We also get into the technical details of semantic clustering across millions of sessions, why video is actually a more compact representation than raw DOM for LLM reasoning, and how Milana analyzes sessions from multiple perspectives — user researcher, PM, founder — to surface real pain points. Plus, a bold prediction: analytics dashboards are dying, and the future belongs to agentic systems that don’t just deliver insights but actually own and drive your OKRs.

Topics covered:

  • Why session replays are the ultimate untapped data asset for product teams
  • How vision LLMs unlocked AI-powered analysis of user sessions
  • Just-in-time data structuring: querying unstructured sessions without upfront instrumentation
  • Lessons from building experimentation platforms at Yelp and Airtable
  • Why running more experiments beats having better data
  • Semantic clustering: separating signal from noise across millions of sessions
  • Video vs. DOM vs. events — the best data representation for LLM reasoning
  • Analyzing agent behavior through session replays
  • The death of dashboards and the rise of agentic growth systems
  • User research horror stories and the surprising things users do


Chapters

00:00 Introduction to Rohan and Raghav's Journey
04:47 The Importance of User Research
08:03 Making Solutioning a Science
11:09 Understanding Session Replays and Experimentation
14:50 Defining Sessions and Experimentation Platforms
18:54 The Need for Consistent Metrics
22:11 The Role of Events vs. Session Replays
29:46 Leveraging LLMs for Enhanced Insights
35:04 Determinism vs. Non-Determinism in Data Analysis
37:57 Understanding User vs. Agent Behavior
39:47 The Art of Structuring Data
45:25 Semantic Clustering and Its Importance
47:09 Building Infrastructure for Complex Data
51:24 The Future of User Simulation and Experimentation

]]> In this episode, we sit down with Rohan Katyal and Raghav Sethi, co-founders of Milana, to discuss the shift from passive analytics to the world’s first AI Product Engineer. Rather than just providing another dashboard to monitor, Rohan and Raghav are building an agentic partner that you add to your product to bridge the gap between discovery and deployment. Drawing on their experience at Meta, Yelp, and Airtable, they explore how Milana enables autonomous improvement - turning deep user intelligence into shippable code and structural refinements that act as a tireless extension of your engineering team.

The conversation dives into why session replays — a mature but historically underused technology — are now a powerful data asset thanks to vision LLMs. Raghav explains how session replays are really just high-granularity logging of DOM changes, not screen recordings, and why feeding them through AI unlocks insights that traditional event-based analytics simply can’t capture. The team breaks down how they use just-in-time structuring to extract meaning from dense, unstructured session data without requiring upfront instrumentation.

Rohan shares hard-won lessons from building Yelp’s experimentation platform — including how teams that simply ran more experiments consistently outperformed those with better data resources. They discuss the tension between A/B testing rigor and iteration speed, why most experiments never ship, and how lowering the cost of generating and testing hypotheses changes everything about product development velocity.

We also get into the technical details of semantic clustering across millions of sessions, why video is actually a more compact representation than raw DOM for LLM reasoning, and how Milana analyzes sessions from multiple perspectives — user researcher, PM, founder — to surface real pain points. Plus, a bold prediction: analytics dashboards are dying, and the future belongs to agentic systems that don’t just deliver insights but actually own and drive your OKRs.

Topics covered:

  • Why session replays are the ultimate untapped data asset for product teams
  • How vision LLMs unlocked AI-powered analysis of user sessions
  • Just-in-time data structuring: querying unstructured sessions without upfront instrumentation
  • Lessons from building experimentation platforms at Yelp and Airtable
  • Why running more experiments beats having better data
  • Semantic clustering: separating signal from noise across millions of sessions
  • Video vs. DOM vs. events — the best data representation for LLM reasoning
  • Analyzing agent behavior through session replays
  • The death of dashboards and the rise of agentic growth systems
  • User research horror stories and the surprising things users do


Chapters

00:00 Introduction to Rohan and Raghav's Journey
04:47 The Importance of User Research
08:03 Making Solutioning a Science
11:09 Understanding Session Replays and Experimentation
14:50 Defining Sessions and Experimentation Platforms
18:54 The Need for Consistent Metrics
22:11 The Role of Events vs. Session Replays
29:46 Leveraging LLMs for Enhanced Insights
35:04 Determinism vs. Non-Determinism in Data Analysis
37:57 Understanding User vs. Agent Behavior
39:47 The Art of Structuring Data
45:25 Semantic Clustering and Its Importance
47:09 Building Infrastructure for Complex Data
51:24 The Future of User Simulation and Experimentation

]]> Fri, 24 Apr 2026 09:00:00 -0700 Kostas, Nitay Kostas, Nitay 3601 In this episode, we sit down with Rohan Katyal and Raghav Sethi, co-founders of Milana, to discuss the shift from passive analytics to the world’s first AI Product Engineer. Rather than just providing another dashboard to monitor, Rohan and Raghav are building an agentic partner that you add to your product to bridge the gap between discovery and deployment. Drawing on their experience at Meta, Yelp, and Airtable, they explore how Milana enables autonomous improvement - turning deep user intelligence into shippable code and structural refinements that act as a tireless extension of your engineering team.

The conversation dives into why session replays — a mature but historically underused technology — are now a powerful data asset thanks to vision LLMs. Raghav explains how session replays are really just high-granularity logging of DOM changes, not screen recordings, and why feeding them through AI unlocks insights that traditional event-based analytics simply can’t capture. The team breaks down how they use just-in-time structuring to extract meaning from dense, unstructured session data without requiring upfront instrumentation.

Rohan shares hard-won lessons from building Yelp’s experimentation platform — including how teams that simply ran more experiments consistently outperformed those with better data resources. They discuss the tension between A/B testing rigor and iteration speed, why most experiments never ship, and how lowering the cost of generating and testing hypotheses changes everything about product development velocity.

We also get into the technical details of semantic clustering across millions of sessions, why video is actually a more compact representation than raw DOM for LLM reasoning, and how Milana analyzes sessions from multiple perspectives — user researcher, PM, founder — to surface real pain points. Plus, a bold prediction: analytics dashboards are dying, and the future belongs to agentic systems that don’t just deliver insights but actually own and drive your OKRs.

Topics covered:

  • Why session replays are the ultimate untapped data asset for product teams
  • How vision LLMs unlocked AI-powered analysis of user sessions
  • Just-in-time data structuring: querying unstructured sessions without upfront instrumentation
  • Lessons from building experimentation platforms at Yelp and Airtable
  • Why running more experiments beats having better data
  • Semantic clustering: separating signal from noise across millions of sessions
  • Video vs. DOM vs. events — the best data representation for LLM reasoning
  • Analyzing agent behavior through session replays
  • The death of dashboards and the rise of agentic growth systems
  • User research horror stories and the surprising things users do


Chapters

00:00 Introduction to Rohan and Raghav's Journey
04:47 The Importance of User Research
08:03 Making Solutioning a Science
11:09 Understanding Session Replays and Experimentation
14:50 Defining Sessions and Experimentation Platforms
18:54 The Need for Consistent Metrics
22:11 The Role of Events vs. Session Replays
29:46 Leveraging LLMs for Enhanced Insights
35:04 Determinism vs. Non-Determinism in Data Analysis
37:57 Understanding User vs. Agent Behavior
39:47 The Art of Structuring Data
45:25 Semantic Clustering and Its Importance
47:09 Building Infrastructure for Complex Data
51:24 The Future of User Simulation and Experimentation

]]> session replays, A/B testing, product growth, Milana, vision LLMs, user research, experimentation platforms, semantic clustering, AI agents, product analytics, user behavior, coding agents, startup growth, DOM analysis No From Exabyte Storage to Reactive Backends: Jamie Turner on Building Convex After Dropbox 27 27 From Exabyte Storage to Reactive Backends: Jamie Turner on Building Convex After Dropbox full c26a50ff-ad54-4148-91dc-88019accd6a9 https://techontherocks.show/27 Jamie, a seasoned startup founder and former Dropbox engineer, shares insights on building distributed systems, scaling storage solutions, and the impact of AI on infrastructure and application development. Discover practical lessons from scaling Dropbox, the evolution of data storage, and how Convex is shaping the future of app development.

]]> Jamie, a seasoned startup founder and former Dropbox engineer, shares insights on building distributed systems, scaling storage solutions, and the impact of AI on infrastructure and application development. Discover practical lessons from scaling Dropbox, the evolution of data storage, and how Convex is shaping the future of app development.

]]> Thu, 09 Apr 2026 08:05:25 -0700 Kostas, Nitay Kostas, Nitay 3553 Jamie, a seasoned startup founder and former Dropbox engineer, shares insights on building distributed systems, scaling storage solutions, and the impact of AI on infrastructure and application development. Discover practical lessons from scaling Dropbox, the evolution of data storage, and how Convex is shaping the future of app development.

]]> technology, infrastructure, cloud, systems, data No From Art to Science: Wild Moose and the Future of AI-Powered Debugging 25 25 From Art to Science: Wild Moose and the Future of AI-Powered Debugging full a9554681-5209-4c78-8819-2cc7432ccbc1 https://techontherocks.show/25 In this episode, we sit down with the full founding team of Wild Moose — CEO Yasmin Dunsky, CTO Roei, and VP R&D Tom Tytunovich — to explore how they’re transforming production debugging from an art into a science using AI.

The trio shares their unconventional founding story — from meeting across three different cities to living together for three months in a California Airbnb to stress-test both their idea and their relationship. They discuss how they identified production debugging as a massive unsolved problem before ChatGPT even launched, recognizing that while code generation is fundamentally a text problem, debugging is a search problem that demands a completely different approach.

We dive deep into Wild Moose’s “microagents” architecture — fast, highly optimized AI agents that replicate the muscle memory of senior engineers to automatically investigate production incidents in under a minute. The team explains why accuracy trumps everything in their space (wrong answers are worse than no answers when you’re debugging at 3 AM), how they navigate the speed-cost-quality triangle, and why they built a test-driven approach to validate agents against past incidents.

We also get into the multi-agent vs. single-agent debate, handling multimodal observability data (logs, metrics, traces, dashboards, code), and how the rapidly evolving LLM landscape creates both opportunities and challenges for production AI systems. Plus, the team shares their favorite outage war stories — including a “WatchCat” hack and a three-month hunt for a single rogue bit.

Topics covered:

  • The Wild Moose origin story and the California Airbnb experiment
  • Why production debugging is a search problem, not a text generation problem
  • Microagents: fast, specialized AI agents for incident investigation
  • Building institutional knowledge into AI — capturing engineering muscle memory
  • The speed-cost-quality triangle in real-time AI systems
  • Multi-agent vs. single-agent architectures: when to use what
  • Handling multimodal observability data with LLMs
  • The future of AI SRE and self-healing production environments
  • Favorite outage war stories from the trenches


Chapters

00:00 Introduction to the Wild Moose Team
04:12 The Spark Behind Wild Moose
08:41 Understanding the Debugging Landscape
12:45 The Role of AI in Debugging
17:31 Building Investigative Agents
21:55 Optimizing Workflows and Feedback Loops
29:12 Navigating Complexity in Software Systems
33:42 Adapting to Rapid Changes in AI Technology
40:02 Microagents: The Future of AI Architecture
44:46 Outage Stories: Lessons from the Trenches
50:49 Vision for the Future of AI in Production

]]> In this episode, we sit down with the full founding team of Wild Moose — CEO Yasmin Dunsky, CTO Roei, and VP R&D Tom Tytunovich — to explore how they’re transforming production debugging from an art into a science using AI.

The trio shares their unconventional founding story — from meeting across three different cities to living together for three months in a California Airbnb to stress-test both their idea and their relationship. They discuss how they identified production debugging as a massive unsolved problem before ChatGPT even launched, recognizing that while code generation is fundamentally a text problem, debugging is a search problem that demands a completely different approach.

We dive deep into Wild Moose’s “microagents” architecture — fast, highly optimized AI agents that replicate the muscle memory of senior engineers to automatically investigate production incidents in under a minute. The team explains why accuracy trumps everything in their space (wrong answers are worse than no answers when you’re debugging at 3 AM), how they navigate the speed-cost-quality triangle, and why they built a test-driven approach to validate agents against past incidents.

We also get into the multi-agent vs. single-agent debate, handling multimodal observability data (logs, metrics, traces, dashboards, code), and how the rapidly evolving LLM landscape creates both opportunities and challenges for production AI systems. Plus, the team shares their favorite outage war stories — including a “WatchCat” hack and a three-month hunt for a single rogue bit.

Topics covered:

  • The Wild Moose origin story and the California Airbnb experiment
  • Why production debugging is a search problem, not a text generation problem
  • Microagents: fast, specialized AI agents for incident investigation
  • Building institutional knowledge into AI — capturing engineering muscle memory
  • The speed-cost-quality triangle in real-time AI systems
  • Multi-agent vs. single-agent architectures: when to use what
  • Handling multimodal observability data with LLMs
  • The future of AI SRE and self-healing production environments
  • Favorite outage war stories from the trenches


Chapters

00:00 Introduction to the Wild Moose Team
04:12 The Spark Behind Wild Moose
08:41 Understanding the Debugging Landscape
12:45 The Role of AI in Debugging
17:31 Building Investigative Agents
21:55 Optimizing Workflows and Feedback Loops
29:12 Navigating Complexity in Software Systems
33:42 Adapting to Rapid Changes in AI Technology
40:02 Microagents: The Future of AI Architecture
44:46 Outage Stories: Lessons from the Trenches
50:49 Vision for the Future of AI in Production

]]> Tue, 17 Mar 2026 07:00:00 -0700 Kostas, Nitay Kostas, Nitay 3160 In this episode, we sit down with the full founding team of Wild Moose — CEO Yasmin Dunsky, CTO Roei, and VP R&D Tom Tytunovich — to explore how they’re transforming production debugging from an art into a science using AI.

The trio shares their unconventional founding story — from meeting across three different cities to living together for three months in a California Airbnb to stress-test both their idea and their relationship. They discuss how they identified production debugging as a massive unsolved problem before ChatGPT even launched, recognizing that while code generation is fundamentally a text problem, debugging is a search problem that demands a completely different approach.

We dive deep into Wild Moose’s “microagents” architecture — fast, highly optimized AI agents that replicate the muscle memory of senior engineers to automatically investigate production incidents in under a minute. The team explains why accuracy trumps everything in their space (wrong answers are worse than no answers when you’re debugging at 3 AM), how they navigate the speed-cost-quality triangle, and why they built a test-driven approach to validate agents against past incidents.

We also get into the multi-agent vs. single-agent debate, handling multimodal observability data (logs, metrics, traces, dashboards, code), and how the rapidly evolving LLM landscape creates both opportunities and challenges for production AI systems. Plus, the team shares their favorite outage war stories — including a “WatchCat” hack and a three-month hunt for a single rogue bit.

Topics covered:

  • The Wild Moose origin story and the California Airbnb experiment
  • Why production debugging is a search problem, not a text generation problem
  • Microagents: fast, specialized AI agents for incident investigation
  • Building institutional knowledge into AI — capturing engineering muscle memory
  • The speed-cost-quality triangle in real-time AI systems
  • Multi-agent vs. single-agent architectures: when to use what
  • Handling multimodal observability data with LLMs
  • The future of AI SRE and self-healing production environments
  • Favorite outage war stories from the trenches


Chapters

00:00 Introduction to the Wild Moose Team
04:12 The Spark Behind Wild Moose
08:41 Understanding the Debugging Landscape
12:45 The Role of AI in Debugging
17:31 Building Investigative Agents
21:55 Optimizing Workflows and Feedback Loops
29:12 Navigating Complexity in Software Systems
33:42 Adapting to Rapid Changes in AI Technology
40:02 Microagents: The Future of AI Architecture
44:46 Outage Stories: Lessons from the Trenches
50:49 Vision for the Future of AI in Production

]]> AI debugging, SRE, microagents, production incidents, Wild Moose, observability, LLMs, AI agents, root cause analysis, on-call, incident response, DevOps, site reliability engineering, AI startups No From Notebooks to Production: Xorq’s lockfile Approach for Reproducible, Portable ML Pipelines 24 24 From Notebooks to Production: Xorq’s lockfile Approach for Reproducible, Portable ML Pipelines full ba95ec95-984f-46d9-a319-5dae1ad1a1de https://techontherocks.show/24 In this episode, Hussain shares the story behind xorq: a “lockfile for ML pipelines” that makes notebook work easier to reproduce, debug, and ship. We talk about why the research→production path is still so manual, how schemas (and Arrow) become the contract between systems, and what it takes to run the same pipeline across engines like Snowflake and Databricks. We also dig into escape hatches for imperative code, why feature stores didn’t become the default, and how xorq fits alongside other technologies like Iceberg.

Chapters

00:00 Hussain's Journey in Data Science

06:00 The Need for xorq: Bridging Research and Production

10:38 Challenges in Machine Learning Deployment

17:40 The Role of Lock Files in Data Pipelines

29:51 Understanding Schema Management in Data Systems

34:40 Navigating Declarative and Imperative Transformations

36:39 The Developer's Journey with xorq

38:34 Feature Stores vs. xorq: A Comparative Analysis

43:43 The Future of Feature Stores and Machine Learning

51:41 Reproducibility in Data Pipelines: xorq vs. Git-like Operations

55:47 The Future of xorq and the Data Ecosystem

]]> In this episode, Hussain shares the story behind xorq: a “lockfile for ML pipelines” that makes notebook work easier to reproduce, debug, and ship. We talk about why the research→production path is still so manual, how schemas (and Arrow) become the contract between systems, and what it takes to run the same pipeline across engines like Snowflake and Databricks. We also dig into escape hatches for imperative code, why feature stores didn’t become the default, and how xorq fits alongside other technologies like Iceberg.

Chapters

00:00 Hussain's Journey in Data Science

06:00 The Need for xorq: Bridging Research and Production

10:38 Challenges in Machine Learning Deployment

17:40 The Role of Lock Files in Data Pipelines

29:51 Understanding Schema Management in Data Systems

34:40 Navigating Declarative and Imperative Transformations

36:39 The Developer's Journey with xorq

38:34 Feature Stores vs. xorq: A Comparative Analysis

43:43 The Future of Feature Stores and Machine Learning

51:41 Reproducibility in Data Pipelines: xorq vs. Git-like Operations

55:47 The Future of xorq and the Data Ecosystem

]]> Thu, 29 Jan 2026 07:00:00 -0800 Kostas, Nitay Kostas, Nitay 3446 In this episode, Hussain shares the story behind xorq: a “lockfile for ML pipelines” that makes notebook work easier to reproduce, debug, and ship. We talk about why the research→production path is still so manual, how schemas (and Arrow) become the contract between systems, and what it takes to run the same pipeline across engines like Snowflake and Databricks. We also dig into escape hatches for imperative code, why feature stores didn’t become the default, and how xorq fits alongside other technologies like Iceberg.

Chapters

00:00 Hussain's Journey in Data Science

06:00 The Need for xorq: Bridging Research and Production

10:38 Challenges in Machine Learning Deployment

17:40 The Role of Lock Files in Data Pipelines

29:51 Understanding Schema Management in Data Systems

34:40 Navigating Declarative and Imperative Transformations

36:39 The Developer's Journey with xorq

38:34 Feature Stores vs. xorq: A Comparative Analysis

43:43 The Future of Feature Stores and Machine Learning

51:41 Reproducibility in Data Pipelines: xorq vs. Git-like Operations

55:47 The Future of xorq and the Data Ecosystem

]]> reproducible ML, ML pipelines, lockfile, manifest, pipeline registry, declarative pipelines, IBIS, Arrow, Arrow record batches, ArrowFlight, DataFusion, DuckDB, Polars, Snowflake, Databricks, multi-engine execution, pipeline portability, lineage, schema contracts, schema evolution, fail-fast compilation, UDFs, pandas UDFs, feature stores, semantic layer, temporal joins, data consistency vs computation consistency, git-for-data, Iceberg, Nessie, time travel, MLOps, research-to-production, monitoring from declarations No From pandas to Arrow: Wes McKinney on the Future of Data Infrastructure 23 23 From pandas to Arrow: Wes McKinney on the Future of Data Infrastructure full 8a3afe12-4b64-4672-9339-d7160bc65670 https://techontherocks.show/23 Summary

In this episode of Tech on the Rocks, Kostas and Nitay sit down with Wes McKinney the creator of pandas and co-creator of Apache Arrow and Ibis, and long-time leader in the Python data ecosystem. Wes walks us through his journey from building pandas in 2008 to rethinking how we represent and move columnar data with Arrow, and why Arrow is fundamentally different from file formats like Parquet and ORC.


We get into the future of data file formats, DataFusion and the new generation of query engines, the rise of open data lakes (Iceberg, Delta, Hudi), and why “big metadata” is becoming just as important as big data. Wes also shares candid thoughts on open source sustainability, how companies and infrastructure projects really survive, and how AI coding agents like Claude Code are changing the day-to-day work of software engineers, especially for complex systems work.


If you care about the foundations of modern data infrastructure, or you’ve ever called import pandas as pd, this is an episode you won’t want to miss.

Chapters


00:00 Intro — Wes McKinney & his journey in the Python data ecosystem

02:15 How pandas evolved & why UX first mattered for data science

06:14 Open source sustainability, funding & the Posit model

07:31 From pandas to Datapad, Cloudera & the origins of Apache Arrow and Ibis

13:38 What is Apache Arrow? In‑memory columnar data, batches & schemas

22:23 Inside Arrow IPC — zero‑copy, Flatbuffers & cross‑language interop

24:34 Arrow vs Parquet — columnar memory format vs columnar storage format

29:28 The next generation of columnar file formats & GPU‑friendly encodings

36:03 Big metadata, table formats & the rise of Iceberg/Delta/Hudi

43:05 Rethinking data systems: from big data to DuckDB, Rust & “no JVM” stacks

54:11 DataFusion as a modular Rust query engine for modern startups

57:58 Open source, the composable data stack & why infra is “AI‑resistant”

01:00:07 Vibe‑coding with AI agents — using Claude Code in real projects

01:09:49 AI, open source maintainers & the risks of AI‑generated contributions

01:18:57 Bridging LLMs and data: ADBC, data context & the future of infra + AI

]]> Summary

In this episode of Tech on the Rocks, Kostas and Nitay sit down with Wes McKinney the creator of pandas and co-creator of Apache Arrow and Ibis, and long-time leader in the Python data ecosystem. Wes walks us through his journey from building pandas in 2008 to rethinking how we represent and move columnar data with Arrow, and why Arrow is fundamentally different from file formats like Parquet and ORC.


We get into the future of data file formats, DataFusion and the new generation of query engines, the rise of open data lakes (Iceberg, Delta, Hudi), and why “big metadata” is becoming just as important as big data. Wes also shares candid thoughts on open source sustainability, how companies and infrastructure projects really survive, and how AI coding agents like Claude Code are changing the day-to-day work of software engineers, especially for complex systems work.


If you care about the foundations of modern data infrastructure, or you’ve ever called import pandas as pd, this is an episode you won’t want to miss.

Chapters


00:00 Intro — Wes McKinney & his journey in the Python data ecosystem

02:15 How pandas evolved & why UX first mattered for data science

06:14 Open source sustainability, funding & the Posit model

07:31 From pandas to Datapad, Cloudera & the origins of Apache Arrow and Ibis

13:38 What is Apache Arrow? In‑memory columnar data, batches & schemas

22:23 Inside Arrow IPC — zero‑copy, Flatbuffers & cross‑language interop

24:34 Arrow vs Parquet — columnar memory format vs columnar storage format

29:28 The next generation of columnar file formats & GPU‑friendly encodings

36:03 Big metadata, table formats & the rise of Iceberg/Delta/Hudi

43:05 Rethinking data systems: from big data to DuckDB, Rust & “no JVM” stacks

54:11 DataFusion as a modular Rust query engine for modern startups

57:58 Open source, the composable data stack & why infra is “AI‑resistant”

01:00:07 Vibe‑coding with AI agents — using Claude Code in real projects

01:09:49 AI, open source maintainers & the risks of AI‑generated contributions

01:18:57 Bridging LLMs and data: ADBC, data context & the future of infra + AI

]]> Mon, 01 Dec 2025 10:55:15 -0800 Kostas, Nitay Kostas, Nitay 4925 Summary

In this episode of Tech on the Rocks, Kostas and Nitay sit down with Wes McKinney the creator of pandas and co-creator of Apache Arrow and Ibis, and long-time leader in the Python data ecosystem. Wes walks us through his journey from building pandas in 2008 to rethinking how we represent and move columnar data with Arrow, and why Arrow is fundamentally different from file formats like Parquet and ORC.


We get into the future of data file formats, DataFusion and the new generation of query engines, the rise of open data lakes (Iceberg, Delta, Hudi), and why “big metadata” is becoming just as important as big data. Wes also shares candid thoughts on open source sustainability, how companies and infrastructure projects really survive, and how AI coding agents like Claude Code are changing the day-to-day work of software engineers, especially for complex systems work.


If you care about the foundations of modern data infrastructure, or you’ve ever called import pandas as pd, this is an episode you won’t want to miss.

Chapters


00:00 Intro — Wes McKinney & his journey in the Python data ecosystem

02:15 How pandas evolved & why UX first mattered for data science

06:14 Open source sustainability, funding & the Posit model

07:31 From pandas to Datapad, Cloudera & the origins of Apache Arrow and Ibis

13:38 What is Apache Arrow? In‑memory columnar data, batches & schemas

22:23 Inside Arrow IPC — zero‑copy, Flatbuffers & cross‑language interop

24:34 Arrow vs Parquet — columnar memory format vs columnar storage format

29:28 The next generation of columnar file formats & GPU‑friendly encodings

36:03 Big metadata, table formats & the rise of Iceberg/Delta/Hudi

43:05 Rethinking data systems: from big data to DuckDB, Rust & “no JVM” stacks

54:11 DataFusion as a modular Rust query engine for modern startups

57:58 Open source, the composable data stack & why infra is “AI‑resistant”

01:00:07 Vibe‑coding with AI agents — using Claude Code in real projects

01:09:49 AI, open source maintainers & the risks of AI‑generated contributions

01:18:57 Bridging LLMs and data: ADBC, data context & the future of infra + AI

]]> Wes McKinney, pandas, Apache Arrow, Apache Parquet, DataFusion, DuckDB, file formats, columnar data, data infrastructure, open source, Python data science, data lakes, Apache Iceberg, Delta Lake, query engines, Rust, Posit, Positron IDE, AI coding agents, Claude Code, big metadata No Navigating the Future of AI and Data Infrastructure with Bauplan 22 22 Navigating the Future of AI and Data Infrastructure with Bauplan full 4b20915c-d272-4d07-aba1-d5d908f7cc3b https://techontherocks.show/22 Summary

In this conversation, the founders of Bauplan, Jacopo and Ciro, share their extensive backgrounds in AI and data infrastructure, discussing the evolution of NLP and the challenges faced in the industry. They highlight the importance of data pipelines in AI effectiveness and the complexities of building data infrastructure.

The discussion also covers lessons learned from previous ventures, the shifting dynamics of the AI market, and the need for collaboration between data scientists and engineers. They emphasize the significance of simplicity in data tools and the future of data management focusing on standardization and accessibility.

In this episode

  • Bauplan was founded by experienced professionals in AI and data.
  • Data challenges remain significant despite advancements in AI.
  • Lessons from previous ventures inform current strategies.
  • Building data infrastructure is complex and requires careful planning.
  • Collaboration between data scientists and engineers is essential.
  • Data engineering will resemble more and more software engineering.
  • Simplicity in data tools can enhance user experience.
  • The future of data management will focus on standardization and accessibility.


If you care about making AI features shippable by regular software teams—not just data specialists—this conversation maps the terrain and the trade-offs.


Chapters

00:00 Introduction to Bauplan and Founders' Background
02:27 The Evolution of NLP and AI Challenges
05:05 Shifts in Data and AI Application
07:56 Lessons from Previous Ventures
10:20 The Search Market Landscape
13:05 Behavioral Data's Role in Search
15:52 Building Data Infrastructure vs. Applications
18:22 The Complexity of Data Management
21:03 Bridging the Gap Between Data Science and Engineering
23:39 Challenges in Infrastructure Development
29:52 Navigating the Infrastructure Landscape
32:19 The Pendulum of Centralization and Decentralization
34:00 The Need for Standardization in Data Infrastructure
36:52 Simplifying Data Workflows
40:29 Radical Simplicity in Data Management
45:28 Overcoming Resistance to Change
48:50 The Future of Data Abstractions and Git for Data

]]> Summary

In this conversation, the founders of Bauplan, Jacopo and Ciro, share their extensive backgrounds in AI and data infrastructure, discussing the evolution of NLP and the challenges faced in the industry. They highlight the importance of data pipelines in AI effectiveness and the complexities of building data infrastructure.

The discussion also covers lessons learned from previous ventures, the shifting dynamics of the AI market, and the need for collaboration between data scientists and engineers. They emphasize the significance of simplicity in data tools and the future of data management focusing on standardization and accessibility.

In this episode

  • Bauplan was founded by experienced professionals in AI and data.
  • Data challenges remain significant despite advancements in AI.
  • Lessons from previous ventures inform current strategies.
  • Building data infrastructure is complex and requires careful planning.
  • Collaboration between data scientists and engineers is essential.
  • Data engineering will resemble more and more software engineering.
  • Simplicity in data tools can enhance user experience.
  • The future of data management will focus on standardization and accessibility.


If you care about making AI features shippable by regular software teams—not just data specialists—this conversation maps the terrain and the trade-offs.


Chapters

00:00 Introduction to Bauplan and Founders' Background
02:27 The Evolution of NLP and AI Challenges
05:05 Shifts in Data and AI Application
07:56 Lessons from Previous Ventures
10:20 The Search Market Landscape
13:05 Behavioral Data's Role in Search
15:52 Building Data Infrastructure vs. Applications
18:22 The Complexity of Data Management
21:03 Bridging the Gap Between Data Science and Engineering
23:39 Challenges in Infrastructure Development
29:52 Navigating the Infrastructure Landscape
32:19 The Pendulum of Centralization and Decentralization
34:00 The Need for Standardization in Data Infrastructure
36:52 Simplifying Data Workflows
40:29 Radical Simplicity in Data Management
45:28 Overcoming Resistance to Change
48:50 The Future of Data Abstractions and Git for Data

]]> Mon, 08 Sep 2025 07:30:33 -0700 Kostas, Nitay Kostas, Nitay 3525 Summary

In this conversation, the founders of Bauplan, Jacopo and Ciro, share their extensive backgrounds in AI and data infrastructure, discussing the evolution of NLP and the challenges faced in the industry. They highlight the importance of data pipelines in AI effectiveness and the complexities of building data infrastructure.

The discussion also covers lessons learned from previous ventures, the shifting dynamics of the AI market, and the need for collaboration between data scientists and engineers. They emphasize the significance of simplicity in data tools and the future of data management focusing on standardization and accessibility.

In this episode

  • Bauplan was founded by experienced professionals in AI and data.
  • Data challenges remain significant despite advancements in AI.
  • Lessons from previous ventures inform current strategies.
  • Building data infrastructure is complex and requires careful planning.
  • Collaboration between data scientists and engineers is essential.
  • Data engineering will resemble more and more software engineering.
  • Simplicity in data tools can enhance user experience.
  • The future of data management will focus on standardization and accessibility.


If you care about making AI features shippable by regular software teams—not just data specialists—this conversation maps the terrain and the trade-offs.


Chapters

00:00 Introduction to Bauplan and Founders' Background
02:27 The Evolution of NLP and AI Challenges
05:05 Shifts in Data and AI Application
07:56 Lessons from Previous Ventures
10:20 The Search Market Landscape
13:05 Behavioral Data's Role in Search
15:52 Building Data Infrastructure vs. Applications
18:22 The Complexity of Data Management
21:03 Bridging the Gap Between Data Science and Engineering
23:39 Challenges in Infrastructure Development
29:52 Navigating the Infrastructure Landscape
32:19 The Pendulum of Centralization and Decentralization
34:00 The Need for Standardization in Data Infrastructure
36:52 Simplifying Data Workflows
40:29 Radical Simplicity in Data Management
45:28 Overcoming Resistance to Change
48:50 The Future of Data Abstractions and Git for Data

]]> Bauplan, AI, serverless, data pipelines, data infrastructure, lakehouse, data management, Git for data, FaaS No Email as a Knowledge Graph: Micro CEO Brett on Rebuilding CRM at the Inbox 21 21 Email as a Knowledge Graph: Micro CEO Brett on Rebuilding CRM at the Inbox full 93b57626-9259-432c-b201-b77e69c0f9be https://techontherocks.show/21 Summary

Brett — founder & CEO of Micro — joins Nitay and Kostas to share how he’s turning email into a knowledge graph and rebuilding CRM right inside the inbox. He traces a path from Google’s M&A and Allo product team to Clearbit and Launch House, then digs into why most “inbox zero” workflows fail, how interoperability and AI agents shift power to the interface, and what it takes to design an email experience people actually live in.


What you’ll learn

  • Why email is a system of record—and how Micro converts threads into people, companies, attachments, tasks, and “updates”
  • The wedge: founders’ real workflows (fundraising, hiring, sales) and why CRM belongs in the inbox
  • Product & UX lessons: skeuomorphic first, flexible theming (consumer vs. enterprise), and copy-the-UI-before-evolving-it
  • M&A realities from Google: talent vs. tech vs. business acquisitions, and why culture kills most deals
  • Burnout and agency: why founders report less burnout than big-company roles
  • The next phase: cross-app “updates” (email, LinkedIn DMs, etc.), Salesforce/HubSpot read–write, and agentic automation

Chapters

00:00 Brett's Journey: From Consulting to Tech Innovator

02:41 The Role of Strategy in Tech Companies

05:16 Understanding M&A: Successes and Failures

07:55 The Evolution of AI in Corporate Strategy

10:26 Transitioning to Product Management

13:19 Lessons from Clearbit: Culture and Growth

15:50 The Impact of Burnout on Career Choices

18:15 Finding Fulfillment in Entrepreneurship

21:09 Navigating the B2B Landscape

23:34 The Necessity of Products in a Crisis

33:24 The Unexpected Layoff and New Beginnings

34:39 The Launch House Experience

37:16 Transforming Reality into an Accelerator

39:17 The Evolution of Founders and Content Creation

41:52 Introducing Micro: A New Email Experience

47:02 Extracting Information for Better Workflows

53:49 Integrating with Existing Ecosystems

01:01:16 The Future of Email and AI

]]> Summary

Brett — founder & CEO of Micro — joins Nitay and Kostas to share how he’s turning email into a knowledge graph and rebuilding CRM right inside the inbox. He traces a path from Google’s M&A and Allo product team to Clearbit and Launch House, then digs into why most “inbox zero” workflows fail, how interoperability and AI agents shift power to the interface, and what it takes to design an email experience people actually live in.


What you’ll learn

  • Why email is a system of record—and how Micro converts threads into people, companies, attachments, tasks, and “updates”
  • The wedge: founders’ real workflows (fundraising, hiring, sales) and why CRM belongs in the inbox
  • Product & UX lessons: skeuomorphic first, flexible theming (consumer vs. enterprise), and copy-the-UI-before-evolving-it
  • M&A realities from Google: talent vs. tech vs. business acquisitions, and why culture kills most deals
  • Burnout and agency: why founders report less burnout than big-company roles
  • The next phase: cross-app “updates” (email, LinkedIn DMs, etc.), Salesforce/HubSpot read–write, and agentic automation

Chapters

00:00 Brett's Journey: From Consulting to Tech Innovator

02:41 The Role of Strategy in Tech Companies

05:16 Understanding M&A: Successes and Failures

07:55 The Evolution of AI in Corporate Strategy

10:26 Transitioning to Product Management

13:19 Lessons from Clearbit: Culture and Growth

15:50 The Impact of Burnout on Career Choices

18:15 Finding Fulfillment in Entrepreneurship

21:09 Navigating the B2B Landscape

23:34 The Necessity of Products in a Crisis

33:24 The Unexpected Layoff and New Beginnings

34:39 The Launch House Experience

37:16 Transforming Reality into an Accelerator

39:17 The Evolution of Founders and Content Creation

41:52 Introducing Micro: A New Email Experience

47:02 Extracting Information for Better Workflows

53:49 Integrating with Existing Ecosystems

01:01:16 The Future of Email and AI

]]> Mon, 18 Aug 2025 08:22:06 -0700 Kostas, Nitay Kostas, Nitay 3688 Summary

Brett — founder & CEO of Micro — joins Nitay and Kostas to share how he’s turning email into a knowledge graph and rebuilding CRM right inside the inbox. He traces a path from Google’s M&A and Allo product team to Clearbit and Launch House, then digs into why most “inbox zero” workflows fail, how interoperability and AI agents shift power to the interface, and what it takes to design an email experience people actually live in.


What you’ll learn

  • Why email is a system of record—and how Micro converts threads into people, companies, attachments, tasks, and “updates”
  • The wedge: founders’ real workflows (fundraising, hiring, sales) and why CRM belongs in the inbox
  • Product & UX lessons: skeuomorphic first, flexible theming (consumer vs. enterprise), and copy-the-UI-before-evolving-it
  • M&A realities from Google: talent vs. tech vs. business acquisitions, and why culture kills most deals
  • Burnout and agency: why founders report less burnout than big-company roles
  • The next phase: cross-app “updates” (email, LinkedIn DMs, etc.), Salesforce/HubSpot read–write, and agentic automation

Chapters

00:00 Brett's Journey: From Consulting to Tech Innovator

02:41 The Role of Strategy in Tech Companies

05:16 Understanding M&A: Successes and Failures

07:55 The Evolution of AI in Corporate Strategy

10:26 Transitioning to Product Management

13:19 Lessons from Clearbit: Culture and Growth

15:50 The Impact of Burnout on Career Choices

18:15 Finding Fulfillment in Entrepreneurship

21:09 Navigating the B2B Landscape

23:34 The Necessity of Products in a Crisis

33:24 The Unexpected Layoff and New Beginnings

34:39 The Launch House Experience

37:16 Transforming Reality into an Accelerator

39:17 The Evolution of Founders and Content Creation

41:52 Introducing Micro: A New Email Experience

47:02 Extracting Information for Better Workflows

53:49 Integrating with Existing Ecosystems

01:01:16 The Future of Email and AI

]]> email as knowledge graph, AI email, CRM, inbox zero, product management, Google M&A, acquisitions, Clearbit, Launch House, founder journey, UX, interoperability, Salesforce, HubSpot, AI agents, fundraising, hiring, sales, attachments, links, tasks extraction, LinkedIn DM, WeChat analogy, updates feed, knowledge graph CRM, entrepreneur burnout, cognitive science, strategy vs tactics No Community, Compilers & the Rust Story with Steve Klabnik 20 20 Community, Compilers & the Rust Story with Steve Klabnik full 67fca002-4410-467b-bc85-c27275fb0e7e https://techontherocks.show/20 Summary

Steve Klabnik has spent the last 15 years shaping how developers write code—from teaching Ruby on Rails to stewarding Rust’s explosive growth. In this wide-ranging conversation, Steve joins Kostas and Nitay to unpack the forces behind Rust’s rise and the blueprint for developer-first tooling.

  • From Rails to Rust: How a web-framework luminary fell for a brand-new systems language and helped turn it into today’s go-to for memory-safe, zero-cost abstractions.
  • Community as UX: The inside story of Cargo, humane compiler errors, and why welcoming IRC channels can matter more than benchmarks.
  • Standards vs. Shipping: What Rust borrowed from the web’s rapid-release model—and why six-week cadences beat three-year committee cycles.
  • Three tribes, one language: How dynamic-language devs, functional programmers, and C/C++ veterans each found a home in Rust—and what they contributed in return.
  • Looking ahead: Steve’s watch-list of next-gen languages (Hylo, Zig, Odin) and the lessons Rust’s journey holds for anyone building tools, communities, or startups today.

Whether you’re chasing segfault-free code, dreaming up a new PL, or just curious how open-source movements gain momentum, this episode is packed with insight and practical takeaways.


Chapters

00:00 Introduction and Personal Connection
00:59 Journey from Ruby on Rails to Rust
02:21 Early Programming Experiences and Interests
07:20 Community Dynamics in Programming Languages
13:59 The Importance of Community in Open Source
14:37 How Ruby on Rails and Rust Built Their Communities
21:44 Standardization vs. Unified Development Models
30:55 Community Debt in Programming Languages
36:24 Release Cadence vs. Feature Development
37:36 Rust's Unique Selling Proposition
43:30 Attracting Diverse Programming Communities
52:31 The Future of Systems Programming Languages

]]> Summary

Steve Klabnik has spent the last 15 years shaping how developers write code—from teaching Ruby on Rails to stewarding Rust’s explosive growth. In this wide-ranging conversation, Steve joins Kostas and Nitay to unpack the forces behind Rust’s rise and the blueprint for developer-first tooling.

  • From Rails to Rust: How a web-framework luminary fell for a brand-new systems language and helped turn it into today’s go-to for memory-safe, zero-cost abstractions.
  • Community as UX: The inside story of Cargo, humane compiler errors, and why welcoming IRC channels can matter more than benchmarks.
  • Standards vs. Shipping: What Rust borrowed from the web’s rapid-release model—and why six-week cadences beat three-year committee cycles.
  • Three tribes, one language: How dynamic-language devs, functional programmers, and C/C++ veterans each found a home in Rust—and what they contributed in return.
  • Looking ahead: Steve’s watch-list of next-gen languages (Hylo, Zig, Odin) and the lessons Rust’s journey holds for anyone building tools, communities, or startups today.

Whether you’re chasing segfault-free code, dreaming up a new PL, or just curious how open-source movements gain momentum, this episode is packed with insight and practical takeaways.


Chapters

00:00 Introduction and Personal Connection
00:59 Journey from Ruby on Rails to Rust
02:21 Early Programming Experiences and Interests
07:20 Community Dynamics in Programming Languages
13:59 The Importance of Community in Open Source
14:37 How Ruby on Rails and Rust Built Their Communities
21:44 Standardization vs. Unified Development Models
30:55 Community Debt in Programming Languages
36:24 Release Cadence vs. Feature Development
37:36 Rust's Unique Selling Proposition
43:30 Attracting Diverse Programming Communities
52:31 The Future of Systems Programming Languages

]]> Mon, 28 Jul 2025 06:00:00 -0700 Kostas, Nitay Kostas, Nitay 3542 Summary

Steve Klabnik has spent the last 15 years shaping how developers write code—from teaching Ruby on Rails to stewarding Rust’s explosive growth. In this wide-ranging conversation, Steve joins Kostas and Nitay to unpack the forces behind Rust’s rise and the blueprint for developer-first tooling.

  • From Rails to Rust: How a web-framework luminary fell for a brand-new systems language and helped turn it into today’s go-to for memory-safe, zero-cost abstractions.
  • Community as UX: The inside story of Cargo, humane compiler errors, and why welcoming IRC channels can matter more than benchmarks.
  • Standards vs. Shipping: What Rust borrowed from the web’s rapid-release model—and why six-week cadences beat three-year committee cycles.
  • Three tribes, one language: How dynamic-language devs, functional programmers, and C/C++ veterans each found a home in Rust—and what they contributed in return.
  • Looking ahead: Steve’s watch-list of next-gen languages (Hylo, Zig, Odin) and the lessons Rust’s journey holds for anyone building tools, communities, or startups today.

Whether you’re chasing segfault-free code, dreaming up a new PL, or just curious how open-source movements gain momentum, this episode is packed with insight and practical takeaways.


Chapters

00:00 Introduction and Personal Connection
00:59 Journey from Ruby on Rails to Rust
02:21 Early Programming Experiences and Interests
07:20 Community Dynamics in Programming Languages
13:59 The Importance of Community in Open Source
14:37 How Ruby on Rails and Rust Built Their Communities
21:44 Standardization vs. Unified Development Models
30:55 Community Debt in Programming Languages
36:24 Release Cadence vs. Feature Development
37:36 Rust's Unique Selling Proposition
43:30 Attracting Diverse Programming Communities
52:31 The Future of Systems Programming Languages

]]> Steve Klabnik, Rust, Ruby on Rails, systems programming, memory safety, Cargo, compiler design, zero-cost abstractions, developer tooling, open-source community, release cadence, language governance, functional programming, dynamic languages, C and C++, Hylo, Zig, Odin, startup playbook, humane error messages No How Cloudflare Reinvents Serverless at Global Scale with Josh Howard 19 19 How Cloudflare Reinvents Serverless at Global Scale with Josh Howard full 0cee35e3-7104-4bb0-be5b-1521205cb9d8 https://techontherocks.show/19 Summary

Josh Howard, Senior Engineering Manager at Cloudflare, joins Kostas and Nitay to discuss Cloudflare's innovative serverless platform, Durable Objects, and Workers. 

Learn how Cloudflare enables developers to build stateful applications with global scale, consistency, and simplicity at the network edge.

Chapters

00:00 Introduction and Background
02:01 Journey into Storage Systems
04:24 Cloudflare's Evolution and Developer Platform
06:29 Understanding Durable Objects
08:57 Durable Objects in Modern App Development
11:18 Use Cases for Cloudflare's Developer Platform
13:36 Building Agents and Real-Time Applications
16:19 Developer Experience and Migration Strategies
25:09 Exploring Workflow Systems: OLAP vs Applications
26:47 Cloudflare's Development Platform: Future Offerings for Data Professionals
28:42 Transitioning from Data Processing to Application Development
31:37 The Impact of LLMs on System Design
33:44 Serverless Platforms: Challenges and Limitations
40:01 Future Directions: Cloudflare's Storage Relay Service and Global Expansion

Click here to view the episode transcript.

]]> Summary

Josh Howard, Senior Engineering Manager at Cloudflare, joins Kostas and Nitay to discuss Cloudflare's innovative serverless platform, Durable Objects, and Workers. 

Learn how Cloudflare enables developers to build stateful applications with global scale, consistency, and simplicity at the network edge.

Chapters

00:00 Introduction and Background
02:01 Journey into Storage Systems
04:24 Cloudflare's Evolution and Developer Platform
06:29 Understanding Durable Objects
08:57 Durable Objects in Modern App Development
11:18 Use Cases for Cloudflare's Developer Platform
13:36 Building Agents and Real-Time Applications
16:19 Developer Experience and Migration Strategies
25:09 Exploring Workflow Systems: OLAP vs Applications
26:47 Cloudflare's Development Platform: Future Offerings for Data Professionals
28:42 Transitioning from Data Processing to Application Development
31:37 The Impact of LLMs on System Design
33:44 Serverless Platforms: Challenges and Limitations
40:01 Future Directions: Cloudflare's Storage Relay Service and Global Expansion

Click here to view the episode transcript.

]]> Thu, 05 Jun 2025 08:41:00 -0700 Kostas, Nitay Kostas, Nitay 3139 Summary

Josh Howard, Senior Engineering Manager at Cloudflare, joins Kostas and Nitay to discuss Cloudflare's innovative serverless platform, Durable Objects, and Workers. 

Learn how Cloudflare enables developers to build stateful applications with global scale, consistency, and simplicity at the network edge.

Chapters

00:00 Introduction and Background
02:01 Journey into Storage Systems
04:24 Cloudflare's Evolution and Developer Platform
06:29 Understanding Durable Objects
08:57 Durable Objects in Modern App Development
11:18 Use Cases for Cloudflare's Developer Platform
13:36 Building Agents and Real-Time Applications
16:19 Developer Experience and Migration Strategies
25:09 Exploring Workflow Systems: OLAP vs Applications
26:47 Cloudflare's Development Platform: Future Offerings for Data Professionals
28:42 Transitioning from Data Processing to Application Development
31:37 The Impact of LLMs on System Design
33:44 Serverless Platforms: Challenges and Limitations
40:01 Future Directions: Cloudflare's Storage Relay Service and Global Expansion

Click here to view the episode transcript.

]]> Cloudflare, Durable Objects, Serverless Computing, Edge Computing, Workers, Global Scale, JavaScript, WebAssembly, State Management, Distributed Systems, Application Development, Real-Time Apps, Consistency, Developer Platform No Business Physics: How Brand, Pricing, and Product Design Define Success with Erik Swan 18 18 Business Physics: How Brand, Pricing, and Product Design Define Success with Erik Swan full 286c722b-bc32-49fd-a812-2358d1184c3a https://techontherocks.show/18 Summary
In this episode, Erik reflects on his long and storied tech career—from the days of punch cards to founding multiple startups, including a stint at Splunk.

At 61, he offers a unique perspective on how the industry has evolved and shares candid insights into what it takes to build a successful company. He discusses the evolution from building simple tools to creating comprehensive solutions and eventually platforms, emphasizing the importance of starting with a “hammer”—a focused, simple tool—before scaling to a broader offering.

Eril introduces his concept of the “physics of business,” a framework for understanding go-to-market dynamics, pricing, and the critical role of brand in differentiating a product in a crowded market.

He also touches on the challenges of product-led growth, the importance of achieving a strong “K value” (viral or network effects), and the pitfalls of allowing short-term quarterly pressures to derail long-term vision. Toward the end, he hints at his current project, Bestimer, which aims to apply lessons from his past ventures and leverage modern AI to tackle a massive, data-intensive problem.

Chapters

00:00 Erik's Journey Through Tech History
04:06 The Philosophy of Designing for Success
09:49 Understanding the Physics of Business
14:29 Timing and Luck in Startups
18:09 Lessons Learned from Splunk
23:30 The Power of Brand in Business
28:02 Leveraging AI for Brand Development
32:04 The Resilience of Splunk
36:45 Building a Competitive Edge
37:28 From Tool to Solution
40:59 The Importance of Onboarding
44:32 Navigating Growth and Market Fit
51:11 Innovating with AI: The Next Chapter

]]> Summary
In this episode, Erik reflects on his long and storied tech career—from the days of punch cards to founding multiple startups, including a stint at Splunk.

At 61, he offers a unique perspective on how the industry has evolved and shares candid insights into what it takes to build a successful company. He discusses the evolution from building simple tools to creating comprehensive solutions and eventually platforms, emphasizing the importance of starting with a “hammer”—a focused, simple tool—before scaling to a broader offering.

Eril introduces his concept of the “physics of business,” a framework for understanding go-to-market dynamics, pricing, and the critical role of brand in differentiating a product in a crowded market.

He also touches on the challenges of product-led growth, the importance of achieving a strong “K value” (viral or network effects), and the pitfalls of allowing short-term quarterly pressures to derail long-term vision. Toward the end, he hints at his current project, Bestimer, which aims to apply lessons from his past ventures and leverage modern AI to tackle a massive, data-intensive problem.

Chapters

00:00 Erik's Journey Through Tech History
04:06 The Philosophy of Designing for Success
09:49 Understanding the Physics of Business
14:29 Timing and Luck in Startups
18:09 Lessons Learned from Splunk
23:30 The Power of Brand in Business
28:02 Leveraging AI for Brand Development
32:04 The Resilience of Splunk
36:45 Building a Competitive Edge
37:28 From Tool to Solution
40:59 The Importance of Onboarding
44:32 Navigating Growth and Market Fit
51:11 Innovating with AI: The Next Chapter

]]> Thu, 08 May 2025 07:00:00 -0700 Kostas, Nitay Kostas, Nitay 3691 Summary
In this episode, Erik reflects on his long and storied tech career—from the days of punch cards to founding multiple startups, including a stint at Splunk.

At 61, he offers a unique perspective on how the industry has evolved and shares candid insights into what it takes to build a successful company. He discusses the evolution from building simple tools to creating comprehensive solutions and eventually platforms, emphasizing the importance of starting with a “hammer”—a focused, simple tool—before scaling to a broader offering.

Eril introduces his concept of the “physics of business,” a framework for understanding go-to-market dynamics, pricing, and the critical role of brand in differentiating a product in a crowded market.

He also touches on the challenges of product-led growth, the importance of achieving a strong “K value” (viral or network effects), and the pitfalls of allowing short-term quarterly pressures to derail long-term vision. Toward the end, he hints at his current project, Bestimer, which aims to apply lessons from his past ventures and leverage modern AI to tackle a massive, data-intensive problem.

Chapters

00:00 Erik's Journey Through Tech History
04:06 The Philosophy of Designing for Success
09:49 Understanding the Physics of Business
14:29 Timing and Luck in Startups
18:09 Lessons Learned from Splunk
23:30 The Power of Brand in Business
28:02 Leveraging AI for Brand Development
32:04 The Resilience of Splunk
36:45 Building a Competitive Edge
37:28 From Tool to Solution
40:59 The Importance of Onboarding
44:32 Navigating Growth and Market Fit
51:11 Innovating with AI: The Next Chapter

]]> tech veteran, startup physics, product design, tool vs platform, brand strategy, go-to-market, pricing, network effects, Splunk, venture capital, innovation, incremental growth, AI, digital transformation, entrepreneurial journey No Incremental Materialization: Reinventing Database Views with Gilad Kleinman of Epsio 17 17 Incremental Materialization: Reinventing Database Views with Gilad Kleinman of Epsio full 1554f0b9-4b08-43ce-9dcb-361a358ea16a https://techontherocks.show/17 Summary


In this episode, Gilad Kleinman, co-founder of Epsio, shares his unique journey from PHP development to low-level kernel programming and how that evolution led him to build an innovative incremental views engine. 

Gilad explains that Epsio tackles a common challenge in databases: making heavy, complex queries faster and more efficient through incremental materialization. He describes how traditional materialized views fall short—often requiring full refreshes—and how Epsio seamlessly integrates with existing databases by consuming replication streams (CDC) and writing back to result tables without disrupting the core transactional system. 

The conversation dives into the technical trade-offs and optimizations involved, such as handling stateful versus stateless operators (like group-by and window functions), using Rust for performance, and the challenges of ensuring consistency. 

Gilad also contrasts Epsio’s approach with streaming systems like Flink, emphasizing that by maintaining tight integration with the native database, Epsio can offer immediate, up-to-date query results while minimizing disruption. 

Finally, he outlines his vision for the future of incremental stream processing and materialized views as a means to reduce compute costs and enhance overall system performance.


Chapters

00:00 From PHP to Kernel Development: A Journey
07:30 Introducing Epsio: The Incremental Views Engine
10:56 The Importance of Materialized Views
15:07 Understanding Incremental Materialization
19:21 Optimizing Query Performance with Epsio
24:53 Integrating Epsio with Existing Databases
27:02 The Shift from Theory to Practice in Data Processing
29:42 Seamless Integration with Existing Databases
32:02 Understanding Epsio Incremental Processing Mechanism
34:46 Challenges and Limitations of Incremental Views
36:49 The Complexity of Implementing Operators
39:56 Trade-offs in Incremental Computation
41:21 User Interaction with Epsio
43:01 Comparing EPSIO with Streaming Systems
45:09 Architectural Guarantees of Epsio
50:33 The Future of Incremental Data Processing

]]> Summary


In this episode, Gilad Kleinman, co-founder of Epsio, shares his unique journey from PHP development to low-level kernel programming and how that evolution led him to build an innovative incremental views engine. 

Gilad explains that Epsio tackles a common challenge in databases: making heavy, complex queries faster and more efficient through incremental materialization. He describes how traditional materialized views fall short—often requiring full refreshes—and how Epsio seamlessly integrates with existing databases by consuming replication streams (CDC) and writing back to result tables without disrupting the core transactional system. 

The conversation dives into the technical trade-offs and optimizations involved, such as handling stateful versus stateless operators (like group-by and window functions), using Rust for performance, and the challenges of ensuring consistency. 

Gilad also contrasts Epsio’s approach with streaming systems like Flink, emphasizing that by maintaining tight integration with the native database, Epsio can offer immediate, up-to-date query results while minimizing disruption. 

Finally, he outlines his vision for the future of incremental stream processing and materialized views as a means to reduce compute costs and enhance overall system performance.


Chapters

00:00 From PHP to Kernel Development: A Journey
07:30 Introducing Epsio: The Incremental Views Engine
10:56 The Importance of Materialized Views
15:07 Understanding Incremental Materialization
19:21 Optimizing Query Performance with Epsio
24:53 Integrating Epsio with Existing Databases
27:02 The Shift from Theory to Practice in Data Processing
29:42 Seamless Integration with Existing Databases
32:02 Understanding Epsio Incremental Processing Mechanism
34:46 Challenges and Limitations of Incremental Views
36:49 The Complexity of Implementing Operators
39:56 Trade-offs in Incremental Computation
41:21 User Interaction with Epsio
43:01 Comparing EPSIO with Streaming Systems
45:09 Architectural Guarantees of Epsio
50:33 The Future of Incremental Data Processing

]]> Thu, 24 Apr 2025 07:14:00 -0700 Kostas, Nitay Kostas, Nitay 3139 Summary


In this episode, Gilad Kleinman, co-founder of Epsio, shares his unique journey from PHP development to low-level kernel programming and how that evolution led him to build an innovative incremental views engine. 

Gilad explains that Epsio tackles a common challenge in databases: making heavy, complex queries faster and more efficient through incremental materialization. He describes how traditional materialized views fall short—often requiring full refreshes—and how Epsio seamlessly integrates with existing databases by consuming replication streams (CDC) and writing back to result tables without disrupting the core transactional system. 

The conversation dives into the technical trade-offs and optimizations involved, such as handling stateful versus stateless operators (like group-by and window functions), using Rust for performance, and the challenges of ensuring consistency. 

Gilad also contrasts Epsio’s approach with streaming systems like Flink, emphasizing that by maintaining tight integration with the native database, Epsio can offer immediate, up-to-date query results while minimizing disruption. 

Finally, he outlines his vision for the future of incremental stream processing and materialized views as a means to reduce compute costs and enhance overall system performance.


Chapters

00:00 From PHP to Kernel Development: A Journey
07:30 Introducing Epsio: The Incremental Views Engine
10:56 The Importance of Materialized Views
15:07 Understanding Incremental Materialization
19:21 Optimizing Query Performance with Epsio
24:53 Integrating Epsio with Existing Databases
27:02 The Shift from Theory to Practice in Data Processing
29:42 Seamless Integration with Existing Databases
32:02 Understanding Epsio Incremental Processing Mechanism
34:46 Challenges and Limitations of Incremental Views
36:49 The Complexity of Implementing Operators
39:56 Trade-offs in Incremental Computation
41:21 User Interaction with Epsio
43:01 Comparing EPSIO with Streaming Systems
45:09 Architectural Guarantees of Epsio
50:33 The Future of Incremental Data Processing

]]> Epsio, incremental materialized views, FCO, database optimization, materialization, CDC, replication stream, SQL, Rust, performance trade-offs, window functions, stateful operators, Postgres, MySQL, streaming systems, Flink, low-level development, kernel programming, incremental computation, query performance, database integration No From Data Mesh to Lake House: Revolutionizing Metadata with Lakekeeper 16 16 From Data Mesh to Lake House: Revolutionizing Metadata with Lakekeeper full ea0f721a-8d0a-423b-a4ea-c0c7816eee4a https://techontherocks.show/16 Summary

In this episode, Viktor Kessler shares his journey and insights from his extensive experience in data management—from building risk management systems and data warehouses to working as a solutions architect at MongoDB and Dremio, and now co-founding a startup.

Initially exploring data mesh concepts, Viktor explains how real-world challenges—such as the disconnect between technical data models and business needs, inconsistent definitions across departments, and the difficulty in managing actionable metadata—led him and his co-founder to pivot toward building a lake house solution.

His startup is developing Lakekeeper, an open source REST catalog for Apache Iceberg, which aims to bridge the gap between decentralized data production and centralized metadata management.

The conversation also delves into the evolution of data catalogs, the necessity for self-service analytics, and how creating consumption-ready data products can transform data functions from cost centers into profit centers.

Finally, Viktor outlines ways for interested listeners to get involved with the Lakekeeper community through GitHub, upcoming meetups, and a dedicated Discord channel.

Chapters

00:00 Introduction to Viktor Kessler and His Journey
04:57 Transitioning from Data Mesh to Lake House
09:15 Understanding Data Mesh: Pain Points and Solutions
13:47 The Role of Metadata in Data Management
18:16 The Evolution of Catalogs and Metadata Management
28:14 Stabilizing the Consumption Pipeline
31:18 Centralizing Metadata for Decentralized Organizations
37:09 Bridging the Gap: Technical and Business Perspectives
43:17 Rethinking Data Products and Consumption
50:45 Finding Balance: Control and Flexibility in Data Management

]]> Summary

In this episode, Viktor Kessler shares his journey and insights from his extensive experience in data management—from building risk management systems and data warehouses to working as a solutions architect at MongoDB and Dremio, and now co-founding a startup.

Initially exploring data mesh concepts, Viktor explains how real-world challenges—such as the disconnect between technical data models and business needs, inconsistent definitions across departments, and the difficulty in managing actionable metadata—led him and his co-founder to pivot toward building a lake house solution.

His startup is developing Lakekeeper, an open source REST catalog for Apache Iceberg, which aims to bridge the gap between decentralized data production and centralized metadata management.

The conversation also delves into the evolution of data catalogs, the necessity for self-service analytics, and how creating consumption-ready data products can transform data functions from cost centers into profit centers.

Finally, Viktor outlines ways for interested listeners to get involved with the Lakekeeper community through GitHub, upcoming meetups, and a dedicated Discord channel.

Chapters

00:00 Introduction to Viktor Kessler and His Journey
04:57 Transitioning from Data Mesh to Lake House
09:15 Understanding Data Mesh: Pain Points and Solutions
13:47 The Role of Metadata in Data Management
18:16 The Evolution of Catalogs and Metadata Management
28:14 Stabilizing the Consumption Pipeline
31:18 Centralizing Metadata for Decentralized Organizations
37:09 Bridging the Gap: Technical and Business Perspectives
43:17 Rethinking Data Products and Consumption
50:45 Finding Balance: Control and Flexibility in Data Management

]]> Fri, 21 Mar 2025 07:30:00 -0700 Kostas, Nitay Kostas, Nitay 3445 Summary

In this episode, Viktor Kessler shares his journey and insights from his extensive experience in data management—from building risk management systems and data warehouses to working as a solutions architect at MongoDB and Dremio, and now co-founding a startup.

Initially exploring data mesh concepts, Viktor explains how real-world challenges—such as the disconnect between technical data models and business needs, inconsistent definitions across departments, and the difficulty in managing actionable metadata—led him and his co-founder to pivot toward building a lake house solution.

His startup is developing Lakekeeper, an open source REST catalog for Apache Iceberg, which aims to bridge the gap between decentralized data production and centralized metadata management.

The conversation also delves into the evolution of data catalogs, the necessity for self-service analytics, and how creating consumption-ready data products can transform data functions from cost centers into profit centers.

Finally, Viktor outlines ways for interested listeners to get involved with the Lakekeeper community through GitHub, upcoming meetups, and a dedicated Discord channel.

Chapters

00:00 Introduction to Viktor Kessler and His Journey
04:57 Transitioning from Data Mesh to Lake House
09:15 Understanding Data Mesh: Pain Points and Solutions
13:47 The Role of Metadata in Data Management
18:16 The Evolution of Catalogs and Metadata Management
28:14 Stabilizing the Consumption Pipeline
31:18 Centralizing Metadata for Decentralized Organizations
37:09 Bridging the Gap: Technical and Business Perspectives
43:17 Rethinking Data Products and Consumption
50:45 Finding Balance: Control and Flexibility in Data Management

]]> data mesh, lake house, Lakekeeper, Apache Iceberg, metadata, decentralized data, data catalog, data governance, data product, actionable metadata, data warehousing, self-service analytics, data transformation, open source, risk management, MongoDB, Dremio, data startup No Reinventing Stream Processing: From LinkedIn to Responsive with Apurva Mehta 15 15 Reinventing Stream Processing: From LinkedIn to Responsive with Apurva Mehta full c47a6b60-94e7-4883-954d-f4d03c46f6aa https://techontherocks.show/15 Summary


In this episode, Apurva Mehta, co-founder and CEO of Responsive, recounts his extensive journey in stream processing—from his early work at LinkedIn and Confluent to his current venture at Responsive.

He explains how stream processing evolved from simple event ingestion and graph indexing to powering complex, stateful applications such as search indexing, inventory management, and trade settlement.

Apurva clarifies the often-misunderstood concept of “real time,” arguing that low latency (often in the one- to two-second range) is more accurate for many applications than the instantaneous response many assume. He delves into the challenges of state management, discussing the limitations of embedded state stores like RocksDB and traditional databases (e.g., Postgres) when faced with high update rates and complex transactional requirements.

The conversation also covers the trade-offs between SQL-based streaming interfaces and more flexible APIs, and how Responsive is innovating by decoupling state from compute—leveraging remote state solutions built on object stores (like S3) with specialized systems such as SlateDB—to improve elasticity, cost efficiency, and operational simplicity in mission-critical applications.

Chapters

00:00 Introduction to Apurva Mehta and Streaming Background
08:50 Defining Real-Time in Streaming Contexts
14:18 Challenges of Stateful Stream Processing
19:50 Comparing Streaming Processing with Traditional Databases
26:38 Product Perspectives on Streaming vs Analytical Systems
31:10 Operational Rigor and Business Opportunities
38:31 Developers' Needs: Beyond SQL
45:53 Simplifying Infrastructure: The Cost of Complexity
51:03 The Future of Streaming Applications

Click here to view the episode transcript.

]]> Summary


In this episode, Apurva Mehta, co-founder and CEO of Responsive, recounts his extensive journey in stream processing—from his early work at LinkedIn and Confluent to his current venture at Responsive.

He explains how stream processing evolved from simple event ingestion and graph indexing to powering complex, stateful applications such as search indexing, inventory management, and trade settlement.

Apurva clarifies the often-misunderstood concept of “real time,” arguing that low latency (often in the one- to two-second range) is more accurate for many applications than the instantaneous response many assume. He delves into the challenges of state management, discussing the limitations of embedded state stores like RocksDB and traditional databases (e.g., Postgres) when faced with high update rates and complex transactional requirements.

The conversation also covers the trade-offs between SQL-based streaming interfaces and more flexible APIs, and how Responsive is innovating by decoupling state from compute—leveraging remote state solutions built on object stores (like S3) with specialized systems such as SlateDB—to improve elasticity, cost efficiency, and operational simplicity in mission-critical applications.

Chapters

00:00 Introduction to Apurva Mehta and Streaming Background
08:50 Defining Real-Time in Streaming Contexts
14:18 Challenges of Stateful Stream Processing
19:50 Comparing Streaming Processing with Traditional Databases
26:38 Product Perspectives on Streaming vs Analytical Systems
31:10 Operational Rigor and Business Opportunities
38:31 Developers' Needs: Beyond SQL
45:53 Simplifying Infrastructure: The Cost of Complexity
51:03 The Future of Streaming Applications

Click here to view the episode transcript.

]]> Thu, 06 Mar 2025 07:29:00 -0800 Kostas, Nitay Kostas, Nitay 3493 Summary


In this episode, Apurva Mehta, co-founder and CEO of Responsive, recounts his extensive journey in stream processing—from his early work at LinkedIn and Confluent to his current venture at Responsive.

He explains how stream processing evolved from simple event ingestion and graph indexing to powering complex, stateful applications such as search indexing, inventory management, and trade settlement.

Apurva clarifies the often-misunderstood concept of “real time,” arguing that low latency (often in the one- to two-second range) is more accurate for many applications than the instantaneous response many assume. He delves into the challenges of state management, discussing the limitations of embedded state stores like RocksDB and traditional databases (e.g., Postgres) when faced with high update rates and complex transactional requirements.

The conversation also covers the trade-offs between SQL-based streaming interfaces and more flexible APIs, and how Responsive is innovating by decoupling state from compute—leveraging remote state solutions built on object stores (like S3) with specialized systems such as SlateDB—to improve elasticity, cost efficiency, and operational simplicity in mission-critical applications.

Chapters

00:00 Introduction to Apurva Mehta and Streaming Background
08:50 Defining Real-Time in Streaming Contexts
14:18 Challenges of Stateful Stream Processing
19:50 Comparing Streaming Processing with Traditional Databases
26:38 Product Perspectives on Streaming vs Analytical Systems
31:10 Operational Rigor and Business Opportunities
38:31 Developers' Needs: Beyond SQL
45:53 Simplifying Infrastructure: The Cost of Complexity
51:03 The Future of Streaming Applications

Click here to view the episode transcript.

]]> stream processing, Kafka, stateful processing, low latency, real-time, Kafka Streams, RocksDB, Responsive, remote state, SlateDB, S3, Confluent, LinkedIn, distributed systems, embedded systems, transactionality, application development, state management, stream processing frameworks, disaster recovery, operational efficiency No Semantic Layers: The Missing Link Between AI and Data with David Jayatillake from Cube 14 14 Semantic Layers: The Missing Link Between AI and Data with David Jayatillake from Cube full d1125907-4784-4c6a-97ef-1a984d5152e5 https://techontherocks.show/14 In this episode, we chat with David Jayatillake, VP of AI at Cube, about semantic layers and their crucial role in making AI work reliably with data. 

We explore how semantic layers act as a bridge between raw data and business meaning, and why they're more practical than pure knowledge graphs. 

David shares insights from his experience at Delphi Labs, where they achieved 100% accuracy in natural language data queries by combining semantic layers with AI, compared to just 16% accuracy with direct text-to-SQL approaches. 

We discuss the challenges of building and maintaining semantic layers, the importance of proper naming and documentation, and how AI can help automate their creation. 

Finally, we explore the future of semantic layers in the context of AI agents and enterprise data systems, and learn about Cube's upcoming AI-powered features for 2025.

00:00 Introduction to AI and Semantic Layers
05:09 The Evolution of Semantic Layers Before and After AI
09:48 Challenges in Implementing Semantic Layers
14:11 The Role of Semantic Layers in Data Access
18:59 The Future of Semantic Layers with AI
23:25 Comparing Text to SQL and Semantic Layer Approaches
27:40 Limitations and Constraints of Semantic Layers
30:08 Understanding LLMs and Semantic Errors
35:03 The Importance of Naming in Semantic Layers
37:07 Debugging Semantic Issues in LLMs
38:07 The Future of LLMs as Agents
41:53 Discovering Services for LLM Agents
50:34 What's Next for Cube and AI Integration

]]> In this episode, we chat with David Jayatillake, VP of AI at Cube, about semantic layers and their crucial role in making AI work reliably with data. 

We explore how semantic layers act as a bridge between raw data and business meaning, and why they're more practical than pure knowledge graphs. 

David shares insights from his experience at Delphi Labs, where they achieved 100% accuracy in natural language data queries by combining semantic layers with AI, compared to just 16% accuracy with direct text-to-SQL approaches. 

We discuss the challenges of building and maintaining semantic layers, the importance of proper naming and documentation, and how AI can help automate their creation. 

Finally, we explore the future of semantic layers in the context of AI agents and enterprise data systems, and learn about Cube's upcoming AI-powered features for 2025.

00:00 Introduction to AI and Semantic Layers
05:09 The Evolution of Semantic Layers Before and After AI
09:48 Challenges in Implementing Semantic Layers
14:11 The Role of Semantic Layers in Data Access
18:59 The Future of Semantic Layers with AI
23:25 Comparing Text to SQL and Semantic Layer Approaches
27:40 Limitations and Constraints of Semantic Layers
30:08 Understanding LLMs and Semantic Errors
35:03 The Importance of Naming in Semantic Layers
37:07 Debugging Semantic Issues in LLMs
38:07 The Future of LLMs as Agents
41:53 Discovering Services for LLM Agents
50:34 What's Next for Cube and AI Integration

]]> Thu, 20 Feb 2025 11:05:41 -0800 Kostas, Nitay Kostas, Nitay 3543 In this episode, we chat with David Jayatillake, VP of AI at Cube, about semantic layers and their crucial role in making AI work reliably with data. 

We explore how semantic layers act as a bridge between raw data and business meaning, and why they're more practical than pure knowledge graphs. 

David shares insights from his experience at Delphi Labs, where they achieved 100% accuracy in natural language data queries by combining semantic layers with AI, compared to just 16% accuracy with direct text-to-SQL approaches. 

We discuss the challenges of building and maintaining semantic layers, the importance of proper naming and documentation, and how AI can help automate their creation. 

Finally, we explore the future of semantic layers in the context of AI agents and enterprise data systems, and learn about Cube's upcoming AI-powered features for 2025.

00:00 Introduction to AI and Semantic Layers
05:09 The Evolution of Semantic Layers Before and After AI
09:48 Challenges in Implementing Semantic Layers
14:11 The Role of Semantic Layers in Data Access
18:59 The Future of Semantic Layers with AI
23:25 Comparing Text to SQL and Semantic Layer Approaches
27:40 Limitations and Constraints of Semantic Layers
30:08 Understanding LLMs and Semantic Errors
35:03 The Importance of Naming in Semantic Layers
37:07 Debugging Semantic Issues in LLMs
38:07 The Future of LLMs as Agents
41:53 Discovering Services for LLM Agents
50:34 What's Next for Cube and AI Integration

]]> semantic layers, AI, data engineering, Cube, text-to-SQL, knowledge graphs, business intelligence, data modeling, data abstraction, BI tools, AI agents, natural language queries, data analytics, OLAP, metadata, data catalog, data governance, data modeling, enterprise data, LLMs, data infrastructure, semantic metadata, data discovery, data transformation, SQL Mesh, DBT, data warehouse, data lineage, AI API, Microsoft Copilot, AWS Q, analytics engineering, data visualization, data architecture, data standardization, data accessibility No From black holes to AI in mathematics: AI Innovation in Mathematics and Health with Yaron Hadad 13 13 From black holes to AI in mathematics: AI Innovation in Mathematics and Health with Yaron Hadad full 6c967941-33e8-4f02-9acb-897bbbed9b7f https://techontherocks.show/13 In this episode, we chat with Yaron Hadad, a fascinating individual who transitioned from theoretical physics to entrepreneurship.

We explore his groundbreaking work on black holes and gravitational waves, and learn about the Ramanujan Machine - an algorithmic system he helped develop that discovers new mathematical formulas and democratizes mathematical research. We'll hear about the scientific community's mixed reactions to this innovative approach.

The conversation then shifts to his work with Neutrino, a company he founded that uses AI and continuous monitoring devices to understand how food affects individual health. We delve into the complexities of nutrition science, the challenges of processing multiple data streams, and the future of personalized health monitoring.

Throughout the episode, Yaron shares insights on bridging theoretical research with practical applications, and the role of AI in advancing both pure mathematics and healthcare.

00:00 Yaron Hadad's Journey: From Physics to AI in Healthcare
04:50 The Complexity of Einstein's Equations and Their Solutions
10:12 AI in Mathematics: The Ramanujan Machine and Conjectures
15:41 Navigating Criticism: The Scientific Community's Response to Innovation
29:24 The Impact of Algorithms in Mathematics
35:30 The Planck Machine: A New Approach
41:15 Neutrino: A Personal Journey in Nutrition
50:11 Connecting Food Complexity to Health Metrics

]]> In this episode, we chat with Yaron Hadad, a fascinating individual who transitioned from theoretical physics to entrepreneurship.

We explore his groundbreaking work on black holes and gravitational waves, and learn about the Ramanujan Machine - an algorithmic system he helped develop that discovers new mathematical formulas and democratizes mathematical research. We'll hear about the scientific community's mixed reactions to this innovative approach.

The conversation then shifts to his work with Neutrino, a company he founded that uses AI and continuous monitoring devices to understand how food affects individual health. We delve into the complexities of nutrition science, the challenges of processing multiple data streams, and the future of personalized health monitoring.

Throughout the episode, Yaron shares insights on bridging theoretical research with practical applications, and the role of AI in advancing both pure mathematics and healthcare.

00:00 Yaron Hadad's Journey: From Physics to AI in Healthcare
04:50 The Complexity of Einstein's Equations and Their Solutions
10:12 AI in Mathematics: The Ramanujan Machine and Conjectures
15:41 Navigating Criticism: The Scientific Community's Response to Innovation
29:24 The Impact of Algorithms in Mathematics
35:30 The Planck Machine: A New Approach
41:15 Neutrino: A Personal Journey in Nutrition
50:11 Connecting Food Complexity to Health Metrics

]]> Tue, 04 Feb 2025 07:33:00 -0800 Kostas, Nitay Kostas, Nitay 3564 In this episode, we chat with Yaron Hadad, a fascinating individual who transitioned from theoretical physics to entrepreneurship.

We explore his groundbreaking work on black holes and gravitational waves, and learn about the Ramanujan Machine - an algorithmic system he helped develop that discovers new mathematical formulas and democratizes mathematical research. We'll hear about the scientific community's mixed reactions to this innovative approach.

The conversation then shifts to his work with Neutrino, a company he founded that uses AI and continuous monitoring devices to understand how food affects individual health. We delve into the complexities of nutrition science, the challenges of processing multiple data streams, and the future of personalized health monitoring.

Throughout the episode, Yaron shares insights on bridging theoretical research with practical applications, and the role of AI in advancing both pure mathematics and healthcare.

00:00 Yaron Hadad's Journey: From Physics to AI in Healthcare
04:50 The Complexity of Einstein's Equations and Their Solutions
10:12 AI in Mathematics: The Ramanujan Machine and Conjectures
15:41 Navigating Criticism: The Scientific Community's Response to Innovation
29:24 The Impact of Algorithms in Mathematics
35:30 The Planck Machine: A New Approach
41:15 Neutrino: A Personal Journey in Nutrition
50:11 Connecting Food Complexity to Health Metrics

]]> AI in mathematics, gravitational waves, mathematical discovery, Ramanujan Machine, AI research, theoretical physics, black holes, Einstein equations, nutrition science, continuous glucose monitoring, personalized health, AI in healthcare, mathematical conjectures, physics research, automated discovery No Building a Native Search Engine in PostgreSQL: ParadeDB's Journey to Replace Elasticsearch with Philippe Noël 12 12 Building a Native Search Engine in PostgreSQL: ParadeDB's Journey to Replace Elasticsearch with Philippe Noël full d4a27eb1-6030-4bcc-8840-c35225b0a740 https://techontherocks.show/12 In this episode, we chat with Philippe Noël, founder of ParadeDB, about building an Elasticsearch alternative natively on PostgreSQL. 

We explore the challenges and benefits of extending PostgreSQL versus building a separate system, diving into topics like full-text search, faceted analytics, and why organizations need these capabilities. 

We discuss the emerging bring-your-own-cloud deployment model, the state of the PostgreSQL extension ecosystem, and what makes a truly production-ready database extension. 

Philippe shares insights on the future of search technology and how recent AI developments are actually increasing the demand for traditional search capabilities. 

The conversation also covers the misconceptions around PostgreSQL's scalability and the trade-offs between multi-tenant and single-tenant architectures in modern data infrastructure.

Chapters

00:00 Introduction to ParadeDB and Its Mission
06:35 User-Facing Search and Analytics
11:45 The Role of Postgres in Modern Data Solutions
17:30 Future of Multimodal Databases
31:04 The Rise of Fintech and Data Integrity
36:36 Deployment Models: BYOC and Control Plane
43:41 The Evolution of Cloud Infrastructure and Serverless Databases
49:38 The Future of Search and Community Engagement

Click here to view the episode transcript.

]]> In this episode, we chat with Philippe Noël, founder of ParadeDB, about building an Elasticsearch alternative natively on PostgreSQL. 

We explore the challenges and benefits of extending PostgreSQL versus building a separate system, diving into topics like full-text search, faceted analytics, and why organizations need these capabilities. 

We discuss the emerging bring-your-own-cloud deployment model, the state of the PostgreSQL extension ecosystem, and what makes a truly production-ready database extension. 

Philippe shares insights on the future of search technology and how recent AI developments are actually increasing the demand for traditional search capabilities. 

The conversation also covers the misconceptions around PostgreSQL's scalability and the trade-offs between multi-tenant and single-tenant architectures in modern data infrastructure.

Chapters

00:00 Introduction to ParadeDB and Its Mission
06:35 User-Facing Search and Analytics
11:45 The Role of Postgres in Modern Data Solutions
17:30 Future of Multimodal Databases
31:04 The Rise of Fintech and Data Integrity
36:36 Deployment Models: BYOC and Control Plane
43:41 The Evolution of Cloud Infrastructure and Serverless Databases
49:38 The Future of Search and Community Engagement

Click here to view the episode transcript.

]]> Thu, 16 Jan 2025 08:30:00 -0800 Kostas, Nitay Kostas, Nitay 3621 In this episode, we chat with Philippe Noël, founder of ParadeDB, about building an Elasticsearch alternative natively on PostgreSQL. 

We explore the challenges and benefits of extending PostgreSQL versus building a separate system, diving into topics like full-text search, faceted analytics, and why organizations need these capabilities. 

We discuss the emerging bring-your-own-cloud deployment model, the state of the PostgreSQL extension ecosystem, and what makes a truly production-ready database extension. 

Philippe shares insights on the future of search technology and how recent AI developments are actually increasing the demand for traditional search capabilities. 

The conversation also covers the misconceptions around PostgreSQL's scalability and the trade-offs between multi-tenant and single-tenant architectures in modern data infrastructure.

Chapters

00:00 Introduction to ParadeDB and Its Mission
06:35 User-Facing Search and Analytics
11:45 The Role of Postgres in Modern Data Solutions
17:30 Future of Multimodal Databases
31:04 The Rise of Fintech and Data Integrity
36:36 Deployment Models: BYOC and Control Plane
43:41 The Evolution of Cloud Infrastructure and Serverless Databases
49:38 The Future of Search and Community Engagement

Click here to view the episode transcript.

]]> PostgreSQL, Elasticsearch alternative, ParadeDB, full-text search, database extensions, faceted search, BM25, bring your own cloud, serverless databases, inverted indexes, Postgres plugins, search engine, database scalability, Postgres replication, search relevancy ranking, database deployment models, TF-IDF, Postgres performance, database infrastructure, data infrastructure No Optimizing SQL with LLMs: Building Verified AI Systems at Espresso AI with Ben Lerner 11 11 Optimizing SQL with LLMs: Building Verified AI Systems at Espresso AI with Ben Lerner full f39c9c6c-f665-4b83-a4ce-f41eb835326d https://techontherocks.show/11 In this episode, we chat with Ben, founder of Espresso AI, about his journey from building Excel Python integrations to optimizing data warehouse compute costs. 

We explore his experience at companies like Uber and Google, where he worked on everything from distributed systems to ML and storage infrastructure. 

We learn about the evolution of his latest venture, which started as a C++ compiler optimization project and transformed into a system for optimizing Snowflake workloads using ML. 

Ben shares insights about applying LLMs to SQL optimization, the challenges of verified code transformation, and the importance of formal verification in ML systems. Finally, we discuss his practical approach to choosing ML models and the critical lesson he learned about talking to users before building products.

Chapters

00:00 Ben's Journey: From Startups to Big Tech
13:00 The Importance of Timing in Entrepreneurship
19:22 Consulting Insights: Learning from Clients
23:32 Transitioning to Big Tech: Experiences at Uber and Google
30:58 The Future of AI: End-to-End Systems and Data Utilization
35:53 Transitioning Between Domains: From ML to Distributed Systems
44:24 Espresso's Mission: Optimizing SQL with ML
51:26 The Future of Code Optimization and AI

Click here to view the episode transcript.

]]> In this episode, we chat with Ben, founder of Espresso AI, about his journey from building Excel Python integrations to optimizing data warehouse compute costs. 

We explore his experience at companies like Uber and Google, where he worked on everything from distributed systems to ML and storage infrastructure. 

We learn about the evolution of his latest venture, which started as a C++ compiler optimization project and transformed into a system for optimizing Snowflake workloads using ML. 

Ben shares insights about applying LLMs to SQL optimization, the challenges of verified code transformation, and the importance of formal verification in ML systems. Finally, we discuss his practical approach to choosing ML models and the critical lesson he learned about talking to users before building products.

Chapters

00:00 Ben's Journey: From Startups to Big Tech
13:00 The Importance of Timing in Entrepreneurship
19:22 Consulting Insights: Learning from Clients
23:32 Transitioning to Big Tech: Experiences at Uber and Google
30:58 The Future of AI: End-to-End Systems and Data Utilization
35:53 Transitioning Between Domains: From ML to Distributed Systems
44:24 Espresso's Mission: Optimizing SQL with ML
51:26 The Future of Code Optimization and AI

Click here to view the episode transcript.

]]> Fri, 03 Jan 2025 09:32:00 -0800 Kostas, Nitay Kostas, Nitay 3964 In this episode, we chat with Ben, founder of Espresso AI, about his journey from building Excel Python integrations to optimizing data warehouse compute costs. 

We explore his experience at companies like Uber and Google, where he worked on everything from distributed systems to ML and storage infrastructure. 

We learn about the evolution of his latest venture, which started as a C++ compiler optimization project and transformed into a system for optimizing Snowflake workloads using ML. 

Ben shares insights about applying LLMs to SQL optimization, the challenges of verified code transformation, and the importance of formal verification in ML systems. Finally, we discuss his practical approach to choosing ML models and the critical lesson he learned about talking to users before building products.

Chapters

00:00 Ben's Journey: From Startups to Big Tech
13:00 The Importance of Timing in Entrepreneurship
19:22 Consulting Insights: Learning from Clients
23:32 Transitioning to Big Tech: Experiences at Uber and Google
30:58 The Future of AI: End-to-End Systems and Data Utilization
35:53 Transitioning Between Domains: From ML to Distributed Systems
44:24 Espresso's Mission: Optimizing SQL with ML
51:26 The Future of Code Optimization and AI

Click here to view the episode transcript.

]]> machine learning, sql optimization, database optimizers, snowflake, data warehouse optimization, LLMs for code, verified ML systems, compiler optimization, VBA, Excel automation, startup journey, Google, Uber, formal verification, code transformation, cost optimization, AI infrastructure, query optimization, query rewriting, DataNitro, YCombinator, scheduling optimization, ML models, Llama, GPU training, ML engineering, distributed systems No Security as Code: Building Developer-First Security Tools with David Mytton 10 10 Security as Code: Building Developer-First Security Tools with David Mytton full fe9366b5-434c-4c34-96af-d5f022fcdd83 https://techontherocks.show/10 In this episode, we chat with David Mytton, founder and CEO of Arcjet and creator of console.dev. 

We explore his journey from building a cloud monitoring startup to founding a security-as-code company. David shares fascinating insights about bot detection, the challenges of securing modern applications, and why traditional security approaches often fail to meet developers' needs. 

We discuss the innovative use of WebAssembly for high-performance security checks, the importance of developer experience in security tools, and the delicate balance between security and latency. 

The conversation also covers his work on environmental technology and cloud computing sustainability, as well as his experience reviewing developer tools for console.dev, where he emphasizes the critical role of documentation in distinguishing great developer tools from mediocre ones.

Chapters

00:00 Introduction to David Mytton and Arcjet
07:09 The Evolution of Observability
12:37 The Future of Observability Tools
18:19 Innovations in Data Storage for Observability
23:57 Challenges in AI Implementation
31:33 The Dichotomy of AI and Human Involvement
36:17 Detecting Bots: Techniques and Challenges
42:46 AI's Role in Enhancing Security
47:52 Latency and Decision-Making in Security
52:40 Managing Software Lifecycle and Observability
58:58 The Role of Documentation in Developer Tools

Click here to view the episode transcript.

]]> In this episode, we chat with David Mytton, founder and CEO of Arcjet and creator of console.dev. 

We explore his journey from building a cloud monitoring startup to founding a security-as-code company. David shares fascinating insights about bot detection, the challenges of securing modern applications, and why traditional security approaches often fail to meet developers' needs. 

We discuss the innovative use of WebAssembly for high-performance security checks, the importance of developer experience in security tools, and the delicate balance between security and latency. 

The conversation also covers his work on environmental technology and cloud computing sustainability, as well as his experience reviewing developer tools for console.dev, where he emphasizes the critical role of documentation in distinguishing great developer tools from mediocre ones.

Chapters

00:00 Introduction to David Mytton and Arcjet
07:09 The Evolution of Observability
12:37 The Future of Observability Tools
18:19 Innovations in Data Storage for Observability
23:57 Challenges in AI Implementation
31:33 The Dichotomy of AI and Human Involvement
36:17 Detecting Bots: Techniques and Challenges
42:46 AI's Role in Enhancing Security
47:52 Latency and Decision-Making in Security
52:40 Managing Software Lifecycle and Observability
58:58 The Role of Documentation in Developer Tools

Click here to view the episode transcript.

]]> Thu, 19 Dec 2024 10:19:00 -0800 Kostas, Nitay Kostas, Nitay 3831 In this episode, we chat with David Mytton, founder and CEO of Arcjet and creator of console.dev. 

We explore his journey from building a cloud monitoring startup to founding a security-as-code company. David shares fascinating insights about bot detection, the challenges of securing modern applications, and why traditional security approaches often fail to meet developers' needs. 

We discuss the innovative use of WebAssembly for high-performance security checks, the importance of developer experience in security tools, and the delicate balance between security and latency. 

The conversation also covers his work on environmental technology and cloud computing sustainability, as well as his experience reviewing developer tools for console.dev, where he emphasizes the critical role of documentation in distinguishing great developer tools from mediocre ones.

Chapters

00:00 Introduction to David Mytton and Arcjet
07:09 The Evolution of Observability
12:37 The Future of Observability Tools
18:19 Innovations in Data Storage for Observability
23:57 Challenges in AI Implementation
31:33 The Dichotomy of AI and Human Involvement
36:17 Detecting Bots: Techniques and Challenges
42:46 AI's Role in Enhancing Security
47:52 Latency and Decision-Making in Security
52:40 Managing Software Lifecycle and Observability
58:58 The Role of Documentation in Developer Tools

Click here to view the episode transcript.

]]> security as code, bot detection, WebAssembly, developer experience, DevTools, Arcjet, console.dev, cloud security, web security, developer tools, documentation, environmental technology, cloud computing sustainability, serverless security, API security, security engineering, bot protection, security automation, developer-first security, real-time security No Dev Environments in the AI Era: Standardizing Development Infrastructure with Daytona's Ivan 9 9 Dev Environments in the AI Era: Standardizing Development Infrastructure with Daytona's Ivan full 1e3d6c9c-0c97-42e9-b487-b0d9519bd279 https://techontherocks.show/9 In this episode, we chat with Ivan, co-founder and CEO of Daytona, about the evolution of developer environments and tooling.

We explore his journey from founding CodeAnywhere in 2009, one of the first browser-based IDEs, to creating the popular Shift developer conference, and now building Daytona's dev environment automation platform. We discuss the changing landscape of development environments, from local-only setups to today's complex hybrid configurations, and why managing these environments has become increasingly challenging.

Ivan shares insights about open source business models, the distinction between users and buyers in dev tools, and what the future holds for AI-assisted development. We also learn about Daytona's unique approach to solving dev environment complexity through standardization and automation, and get Ivan's perspective on the future of IDE companies in an AI-driven world.

Chapters

00:00 Introduction to Ivan and Daytona
07:22 Understanding Development Environments
13:59 The User vs. Buyer Dilemma
22:20 Open Source Strategy and Community Building
29:22 How Daytona Works and Its Value Proposition
37:44 Emerging Trends in Collaborative Coding
44:38 Latency Challenges in AI-Assisted Development
50:41 The Future of Developer Tooling Companies
01:02:29 Lessons from Organizing Conferences

]]> In this episode, we chat with Ivan, co-founder and CEO of Daytona, about the evolution of developer environments and tooling.

We explore his journey from founding CodeAnywhere in 2009, one of the first browser-based IDEs, to creating the popular Shift developer conference, and now building Daytona's dev environment automation platform. We discuss the changing landscape of development environments, from local-only setups to today's complex hybrid configurations, and why managing these environments has become increasingly challenging.

Ivan shares insights about open source business models, the distinction between users and buyers in dev tools, and what the future holds for AI-assisted development. We also learn about Daytona's unique approach to solving dev environment complexity through standardization and automation, and get Ivan's perspective on the future of IDE companies in an AI-driven world.

Chapters

00:00 Introduction to Ivan and Daytona
07:22 Understanding Development Environments
13:59 The User vs. Buyer Dilemma
22:20 Open Source Strategy and Community Building
29:22 How Daytona Works and Its Value Proposition
37:44 Emerging Trends in Collaborative Coding
44:38 Latency Challenges in AI-Assisted Development
50:41 The Future of Developer Tooling Companies
01:02:29 Lessons from Organizing Conferences

]]> Wed, 04 Dec 2024 09:00:00 -0800 Kostas, Nitay Kostas, Nitay 4163 In this episode, we chat with Ivan, co-founder and CEO of Daytona, about the evolution of developer environments and tooling.

We explore his journey from founding CodeAnywhere in 2009, one of the first browser-based IDEs, to creating the popular Shift developer conference, and now building Daytona's dev environment automation platform. We discuss the changing landscape of development environments, from local-only setups to today's complex hybrid configurations, and why managing these environments has become increasingly challenging.

Ivan shares insights about open source business models, the distinction between users and buyers in dev tools, and what the future holds for AI-assisted development. We also learn about Daytona's unique approach to solving dev environment complexity through standardization and automation, and get Ivan's perspective on the future of IDE companies in an AI-driven world.

Chapters

00:00 Introduction to Ivan and Daytona
07:22 Understanding Development Environments
13:59 The User vs. Buyer Dilemma
22:20 Open Source Strategy and Community Building
29:22 How Daytona Works and Its Value Proposition
37:44 Emerging Trends in Collaborative Coding
44:38 Latency Challenges in AI-Assisted Development
50:41 The Future of Developer Tooling Companies
01:02:29 Lessons from Organizing Conferences

]]> development environments, Daytona, CodeAnywhere, browser IDE, dev tools, remote development, developer experience, cloud development, AI coding, dev environment automation, VSCode, Cursor, development infrastructure, containerization, cloud IDE, developer tooling, open source business models, enterprise software, development workflow, cloud native development, dev environment standardization, software infrastructure, developer productivity, cloud computing, development platforms, IDE automation, Ivan Daytona, Shift Conference, tech conferences, developer conferences, developer tools monetization, AI development tools, remote developer tools No Evolving Data Infrastructure for the AI Era: AWS, Meta, and Beyond with Roy Ben-Alta 8 8 Evolving Data Infrastructure for the AI Era: AWS, Meta, and Beyond with Roy Ben-Alta full 60d9fe9e-734f-4ab0-8b19-94ddf3d0b296 https://techontherocks.show/8 In this episode, we chat with Roy Ben-Alta, co-founder of Oakminer AI and former director at Meta AI Research, about his fascinating journey through the evolution of data infrastructure and AI. We explore his early days at AWS when cloud adoption was still controversial, his experience building large language models at Meta, and the challenges of training and deploying AI systems at scale. Roy shares valuable insights about the future of data warehouses, the emergence of knowledge-centric systems, and the critical role of data engineering in AI. We'll also hear his practical advice on building AI companies today, including thoughts on model evaluation frameworks, vendor lock-in, and the eternal "build vs. buy" decision. Drawing from his extensive experience across Amazon, Meta, and now as a founder, Roy offers a unique perspective on how AI is transforming traditional data infrastructure and what it means for the future of enterprise software.

Chapters

00:00 Introduction to Roy Benalta and AI Background
04:07 Warren Buffett Experience and MBA Insights
06:45 Lessons from Amazon and Meta Leadership
09:15 Early Days of AWS and Cloud Adoption
12:12 Redshift vs. Snowflake: A Data Warehouse Perspective
14:49 Navigating Complex Data Systems in Organizations
31:21 The Future of Personalized Software Solutions
32:19 Building Large Language Models at Meta
39:27 Evolution of Data Platforms and Infrastructure
50:50 Engineering Knowledge and LLMs
58:27 Build vs. Buy: Strategic Decisions for Startups

]]> In this episode, we chat with Roy Ben-Alta, co-founder of Oakminer AI and former director at Meta AI Research, about his fascinating journey through the evolution of data infrastructure and AI. We explore his early days at AWS when cloud adoption was still controversial, his experience building large language models at Meta, and the challenges of training and deploying AI systems at scale. Roy shares valuable insights about the future of data warehouses, the emergence of knowledge-centric systems, and the critical role of data engineering in AI. We'll also hear his practical advice on building AI companies today, including thoughts on model evaluation frameworks, vendor lock-in, and the eternal "build vs. buy" decision. Drawing from his extensive experience across Amazon, Meta, and now as a founder, Roy offers a unique perspective on how AI is transforming traditional data infrastructure and what it means for the future of enterprise software.

Chapters

00:00 Introduction to Roy Benalta and AI Background
04:07 Warren Buffett Experience and MBA Insights
06:45 Lessons from Amazon and Meta Leadership
09:15 Early Days of AWS and Cloud Adoption
12:12 Redshift vs. Snowflake: A Data Warehouse Perspective
14:49 Navigating Complex Data Systems in Organizations
31:21 The Future of Personalized Software Solutions
32:19 Building Large Language Models at Meta
39:27 Evolution of Data Platforms and Infrastructure
50:50 Engineering Knowledge and LLMs
58:27 Build vs. Buy: Strategic Decisions for Startups

]]> Thu, 21 Nov 2024 09:00:00 -0800 Kostas, Nitay Kostas, Nitay 3808 In this episode, we chat with Roy Ben-Alta, co-founder of Oakminer AI and former director at Meta AI Research, about his fascinating journey through the evolution of data infrastructure and AI. We explore his early days at AWS when cloud adoption was still controversial, his experience building large language models at Meta, and the challenges of training and deploying AI systems at scale. Roy shares valuable insights about the future of data warehouses, the emergence of knowledge-centric systems, and the critical role of data engineering in AI. We'll also hear his practical advice on building AI companies today, including thoughts on model evaluation frameworks, vendor lock-in, and the eternal "build vs. buy" decision. Drawing from his extensive experience across Amazon, Meta, and now as a founder, Roy offers a unique perspective on how AI is transforming traditional data infrastructure and what it means for the future of enterprise software.

Chapters

00:00 Introduction to Roy Benalta and AI Background
04:07 Warren Buffett Experience and MBA Insights
06:45 Lessons from Amazon and Meta Leadership
09:15 Early Days of AWS and Cloud Adoption
12:12 Redshift vs. Snowflake: A Data Warehouse Perspective
14:49 Navigating Complex Data Systems in Organizations
31:21 The Future of Personalized Software Solutions
32:19 Building Large Language Models at Meta
39:27 Evolution of Data Platforms and Infrastructure
50:50 Engineering Knowledge and LLMs
58:27 Build vs. Buy: Strategic Decisions for Startups

]]> AWS, Meta AI Research, Enterprise AI, LLMs, Data Infrastructure, Knowledge Systems, Data Warehouses, Cloud Computing, AI Infrastructure, Build vs Buy, Model Evaluation, Data Engineering, AI Engineering, Meta AI, Redshift, DynamoDB, AI Observability, Startup Advice, Data Architecture, Machine Learning Infrastructure, Cloud Migration, AI Development, Amazon Web Services, Data Systems Evolution, AI Strategy, ChatGPT, Real-time Data, Data Lake, Knowledge Management, Model Training, GPU Infrastructure, AI Adoption, Data Platform, Enterprise Software, Business Intelligence, Generative AI, AI Evaluation Framework, Prompt Engineering, Data Quality, AI Integration No From Functions to Full Applications: How Serverless Evolved Beyond AWS Lambda with Nitzan Shapira 7 7 From Functions to Full Applications: How Serverless Evolved Beyond AWS Lambda with Nitzan Shapira full 534b3342-a30c-4af8-9a17-a635c848e7a5 https://techontherocks.show/7 In this episode, we chat with Nitzan Shapira, co-founder and former CEO of Epsagon, which was acquired by Cisco in 2021. We explore Nitzan's journey from working in cybersecurity to building an observability platform for cloud applications, particularly focused on serverless architectures. We learn about the early days of serverless adoption, the challenges in making observability tools developer-friendly, and why distributed tracing was a key differentiator for Epsagon. We discuss the evolution of observability tools, the future impact of AI on both observability and software development, and the changing landscape of serverless computing. Finally, we hear Nitzan's current perspective on enterprise AI adoption from his role at Cisco, where he helps evaluate and build new AI-focused business lines.

03:17 Transition from Security to Observability
09:52 Exploring Ideas and Choosing Serverless
16:43 Adoption of Distributed Tracing
20:54 The Future of Observability
25:26 Building a Product that Developers Love
31:03 Challenges in Observability and Data Costs
32:47 The Excitement and Evolution of Serverless
35:44 Serverless as a Horizontal Platform
37:15 The Future of Serverless and No-Code/Low-Code Tools
38:15 Technical Limits and the Future of Serverless
40:38 Navigating Near-Death Moments and Go-to-Market Challenges
48:36 Cisco's Gen .AI Ecosystem and New Business Lines
50:25 The State of the AI Ecosystem and Enterprise Adoption
53:54 Using AI to Enhance Engineering and Product Development
55:02 Using AI in Go-to-Market Strategies

]]> In this episode, we chat with Nitzan Shapira, co-founder and former CEO of Epsagon, which was acquired by Cisco in 2021. We explore Nitzan's journey from working in cybersecurity to building an observability platform for cloud applications, particularly focused on serverless architectures. We learn about the early days of serverless adoption, the challenges in making observability tools developer-friendly, and why distributed tracing was a key differentiator for Epsagon. We discuss the evolution of observability tools, the future impact of AI on both observability and software development, and the changing landscape of serverless computing. Finally, we hear Nitzan's current perspective on enterprise AI adoption from his role at Cisco, where he helps evaluate and build new AI-focused business lines.

03:17 Transition from Security to Observability
09:52 Exploring Ideas and Choosing Serverless
16:43 Adoption of Distributed Tracing
20:54 The Future of Observability
25:26 Building a Product that Developers Love
31:03 Challenges in Observability and Data Costs
32:47 The Excitement and Evolution of Serverless
35:44 Serverless as a Horizontal Platform
37:15 The Future of Serverless and No-Code/Low-Code Tools
38:15 Technical Limits and the Future of Serverless
40:38 Navigating Near-Death Moments and Go-to-Market Challenges
48:36 Cisco's Gen .AI Ecosystem and New Business Lines
50:25 The State of the AI Ecosystem and Enterprise Adoption
53:54 Using AI to Enhance Engineering and Product Development
55:02 Using AI in Go-to-Market Strategies

]]> Wed, 06 Nov 2024 09:18:51 -0800 Kostas, Nitay Kostas, Nitay 3498 In this episode, we chat with Nitzan Shapira, co-founder and former CEO of Epsagon, which was acquired by Cisco in 2021. We explore Nitzan's journey from working in cybersecurity to building an observability platform for cloud applications, particularly focused on serverless architectures. We learn about the early days of serverless adoption, the challenges in making observability tools developer-friendly, and why distributed tracing was a key differentiator for Epsagon. We discuss the evolution of observability tools, the future impact of AI on both observability and software development, and the changing landscape of serverless computing. Finally, we hear Nitzan's current perspective on enterprise AI adoption from his role at Cisco, where he helps evaluate and build new AI-focused business lines.

03:17 Transition from Security to Observability
09:52 Exploring Ideas and Choosing Serverless
16:43 Adoption of Distributed Tracing
20:54 The Future of Observability
25:26 Building a Product that Developers Love
31:03 Challenges in Observability and Data Costs
32:47 The Excitement and Evolution of Serverless
35:44 Serverless as a Horizontal Platform
37:15 The Future of Serverless and No-Code/Low-Code Tools
38:15 Technical Limits and the Future of Serverless
40:38 Navigating Near-Death Moments and Go-to-Market Challenges
48:36 Cisco's Gen .AI Ecosystem and New Business Lines
50:25 The State of the AI Ecosystem and Enterprise Adoption
53:54 Using AI to Enhance Engineering and Product Development
55:02 Using AI in Go-to-Market Strategies

]]> Keywords: Epsagon, Observability, Serverless, AWS Lambda, Distributed Tracing, Developer Experience, Enterprise Adoption, Cisco Acquisition, Production Incidents, Open Telemetry, Customer Success, No-code/Low-code, Vercel, Enterprise AI, Product Implementation, Go-to-market Strategy, Developer Tools, Technical Founders, Cloud Computing, Platform Evolution No From GPU Compilers to architecting Kubernetes: A Conversation with Brian Grant 6 6 From GPU Compilers to architecting Kubernetes: A Conversation with Brian Grant full c01e77c4-dfd5-4f6a-ba6d-fe3f5911476b https://techontherocks.show/6 From GPU computing pioneer to Kubernetes architect, Brian Grant takes us on a fascinating journey through his career at the forefront of systems engineering. In this episode, we explore his early work on GPU compilers in the pre-CUDA era, where he tackled unique challenges in high-performance computing when graphics cards weren't yet designed for general computation. Brian then shares insights from his time at Google, where he helped develop Borg and later became the original lead architect of Kubernetes. He explains key architectural decisions that shaped Kubernetes, from its extensible resource model to its approach to service discovery, and why they chose to create a rich set of abstractions rather than a minimal interface. The conversation concludes with Brian's thoughts on standardization challenges in cloud infrastructure and his vision for moving beyond infrastructure as code, offering valuable perspective on both the history and future of distributed systems.

Links:
Brian Grant LI

Chapters

00:00 Introduction and Background
03:11 Early Work in High-Performance Computing
06:21 Challenges of Building Compilers for GPUs
13:14 Influential Innovations in Compilers
31:46 The Future of Compilers
33:11 The Rise of Niche Programming Languages
34:01 The Evolution of Google's Borg and Kubernetes
39:06 Challenges of Managing Applications in a Dynamically Scheduled Environment
48:12 The Need for Standardization in Application Interfaces and Management Systems
01:00:55 Driving Network Effects and Creating Cohesive Ecosystems

Click here to view the episode transcript.

]]> From GPU computing pioneer to Kubernetes architect, Brian Grant takes us on a fascinating journey through his career at the forefront of systems engineering. In this episode, we explore his early work on GPU compilers in the pre-CUDA era, where he tackled unique challenges in high-performance computing when graphics cards weren't yet designed for general computation. Brian then shares insights from his time at Google, where he helped develop Borg and later became the original lead architect of Kubernetes. He explains key architectural decisions that shaped Kubernetes, from its extensible resource model to its approach to service discovery, and why they chose to create a rich set of abstractions rather than a minimal interface. The conversation concludes with Brian's thoughts on standardization challenges in cloud infrastructure and his vision for moving beyond infrastructure as code, offering valuable perspective on both the history and future of distributed systems.

Links:
Brian Grant LI

Chapters

00:00 Introduction and Background
03:11 Early Work in High-Performance Computing
06:21 Challenges of Building Compilers for GPUs
13:14 Influential Innovations in Compilers
31:46 The Future of Compilers
33:11 The Rise of Niche Programming Languages
34:01 The Evolution of Google's Borg and Kubernetes
39:06 Challenges of Managing Applications in a Dynamically Scheduled Environment
48:12 The Need for Standardization in Application Interfaces and Management Systems
01:00:55 Driving Network Effects and Creating Cohesive Ecosystems

Click here to view the episode transcript.

]]> Tue, 22 Oct 2024 12:23:28 -0700 Kostas, Nitay Kostas, Nitay 3705 From GPU computing pioneer to Kubernetes architect, Brian Grant takes us on a fascinating journey through his career at the forefront of systems engineering. In this episode, we explore his early work on GPU compilers in the pre-CUDA era, where he tackled unique challenges in high-performance computing when graphics cards weren't yet designed for general computation. Brian then shares insights from his time at Google, where he helped develop Borg and later became the original lead architect of Kubernetes. He explains key architectural decisions that shaped Kubernetes, from its extensible resource model to its approach to service discovery, and why they chose to create a rich set of abstractions rather than a minimal interface. The conversation concludes with Brian's thoughts on standardization challenges in cloud infrastructure and his vision for moving beyond infrastructure as code, offering valuable perspective on both the history and future of distributed systems.

Links:
Brian Grant LI

Chapters

00:00 Introduction and Background
03:11 Early Work in High-Performance Computing
06:21 Challenges of Building Compilers for GPUs
13:14 Influential Innovations in Compilers
31:46 The Future of Compilers
33:11 The Rise of Niche Programming Languages
34:01 The Evolution of Google's Borg and Kubernetes
39:06 Challenges of Managing Applications in a Dynamically Scheduled Environment
48:12 The Need for Standardization in Application Interfaces and Management Systems
01:00:55 Driving Network Effects and Creating Cohesive Ecosystems

Click here to view the episode transcript.

]]> Kubernetes, Borg, GPU Computing, CUDA, Compilers, Dynamic Compilation, Static Single Assignment, Container Orchestration, Infrastructure as Code, Service Discovery, Control Plane, Multi-threading, SIMD, VLIW, Distributed Systems, Google, ATI, NVIDIA, Transmeta, LLVM, Docker, System Architecture, Cloud Infrastructure, Standardization, Application Management, Workload Scheduling, Infrastructure Design, API Design, Platform Portability, Cluster Management, Pre-CUDA GPU Computing, Early Container Orchestration, Google's Internal Systems, Evolution of Cloud Computing, Development of Kubernetes No Proving Code Correctness: FizzBee and the Future of Formal Methods in Software Design with FizzBee's creator JP 5 5 Proving Code Correctness: FizzBee and the Future of Formal Methods in Software Design with FizzBee's creator JP full 63b57d93-1697-4e48-a105-5ca85b69e9e8 https://techontherocks.show/5 In this episode, we chat with JP, creator of FizzBee, about formal methods and their application in software engineering. We explore the differences between coding and engineering, discussing how formal methods can improve system design and reliability. JP shares insights from his time at Google and explains why tools like FizzBee are crucial for distributed systems. We delve into the challenges of adopting formal methods in industry, the potential of FizzBee to make these techniques more accessible, and how it compares to other tools like TLA+. Finally, we discuss the future of software development, including the role of LLMs in code generation and the ongoing importance of human engineers in system design.

Links
FizzBee
FizzBee Github Repo
FizzBee Blog

Chapters
00:00 Introduction and Overview
02:42 JP's Experience at Google and the Growth of the Company
04:51 The Difference Between Engineers and Coders
06:41 The Importance of Rigor and Quality in Engineering
10:08 The Limitations of QA and the Need for Formal Methods
14:00 The Role of Best Practices in Software Engineering
14:56 Design Specification Languages for System Correctness
21:43 The Applicability of Formal Methods in Distributed Systems
31:20 Getting Started with FizzBee: A Practical Example
36:06 Common Assumptions and Misconceptions in Distributed Systems
43:23 The Role of FizzBee in the Design Phase
48:04 The Future of FizzBee: LLMs and Code Generation
58:20 Getting Started with FizzBee: Tutorials and Online Playground


Click here to view the episode transcript.

]]> In this episode, we chat with JP, creator of FizzBee, about formal methods and their application in software engineering. We explore the differences between coding and engineering, discussing how formal methods can improve system design and reliability. JP shares insights from his time at Google and explains why tools like FizzBee are crucial for distributed systems. We delve into the challenges of adopting formal methods in industry, the potential of FizzBee to make these techniques more accessible, and how it compares to other tools like TLA+. Finally, we discuss the future of software development, including the role of LLMs in code generation and the ongoing importance of human engineers in system design.

Links
FizzBee
FizzBee Github Repo
FizzBee Blog

Chapters
00:00 Introduction and Overview
02:42 JP's Experience at Google and the Growth of the Company
04:51 The Difference Between Engineers and Coders
06:41 The Importance of Rigor and Quality in Engineering
10:08 The Limitations of QA and the Need for Formal Methods
14:00 The Role of Best Practices in Software Engineering
14:56 Design Specification Languages for System Correctness
21:43 The Applicability of Formal Methods in Distributed Systems
31:20 Getting Started with FizzBee: A Practical Example
36:06 Common Assumptions and Misconceptions in Distributed Systems
43:23 The Role of FizzBee in the Design Phase
48:04 The Future of FizzBee: LLMs and Code Generation
58:20 Getting Started with FizzBee: Tutorials and Online Playground


Click here to view the episode transcript.

]]> Mon, 07 Oct 2024 22:32:24 -0700 Kostas, Nitay Kostas, Nitay 3688 In this episode, we chat with JP, creator of FizzBee, about formal methods and their application in software engineering. We explore the differences between coding and engineering, discussing how formal methods can improve system design and reliability. JP shares insights from his time at Google and explains why tools like FizzBee are crucial for distributed systems. We delve into the challenges of adopting formal methods in industry, the potential of FizzBee to make these techniques more accessible, and how it compares to other tools like TLA+. Finally, we discuss the future of software development, including the role of LLMs in code generation and the ongoing importance of human engineers in system design.

Links
FizzBee
FizzBee Github Repo
FizzBee Blog

Chapters
00:00 Introduction and Overview
02:42 JP's Experience at Google and the Growth of the Company
04:51 The Difference Between Engineers and Coders
06:41 The Importance of Rigor and Quality in Engineering
10:08 The Limitations of QA and the Need for Formal Methods
14:00 The Role of Best Practices in Software Engineering
14:56 Design Specification Languages for System Correctness
21:43 The Applicability of Formal Methods in Distributed Systems
31:20 Getting Started with FizzBee: A Practical Example
36:06 Common Assumptions and Misconceptions in Distributed Systems
43:23 The Role of FizzBee in the Design Phase
48:04 The Future of FizzBee: LLMs and Code Generation
58:20 Getting Started with FizzBee: Tutorials and Online Playground


Click here to view the episode transcript.

]]> Formal methods, FizzBee, Software engineering, Distributed systems, Code verification, System design, TLA+, Model checking, Python, Google engineering practices, Design specification, Fault tolerance, Consistency, LLMs in software development, Test case generation, Software correctness, Automated analysis, Starlark, Iceberg specification, Software development lifecycle No MLOps Evolution: Data, Experiments, and AI with Dean Pleban from DagsHub 4 4 MLOps Evolution: Data, Experiments, and AI with Dean Pleban from DagsHub full 215dd901-50e1-488c-ba46-b8268bc52ae3 https://techontherocks.show/4 In this episode, we chat with Dean Pleban, CEO of DagsHub, about machine learning operations. We explore the differences between DevOps and MLOps, focusing on data management and experiment tracking. Dean shares insights on versioning various components in ML projects and discusses the importance of user experience in MLOps tools. We also touch on DagsHub's integration of AI in their product and Dean's vision for the future of AI and machine learning in industry.

Links

DagsHub
The MLOps Podcast
Dean on LI

Chapters

00:00 Introduction and Background
03:03 Challenges of Managing Machine Learning Projects
10:00 The Concept of Experiments in Machine Learning
12:51 Data Curation and Validation for High-Quality Data
27:07 Connecting the Components of Machine Learning Projects with DAGS Hub
29:12 The Importance of Data and Clear Interfaces
43:29 Incorporating Machine Learning into DAGsHub
51:27 The Future of ML and AI

]]> In this episode, we chat with Dean Pleban, CEO of DagsHub, about machine learning operations. We explore the differences between DevOps and MLOps, focusing on data management and experiment tracking. Dean shares insights on versioning various components in ML projects and discusses the importance of user experience in MLOps tools. We also touch on DagsHub's integration of AI in their product and Dean's vision for the future of AI and machine learning in industry.

Links

DagsHub
The MLOps Podcast
Dean on LI

Chapters

00:00 Introduction and Background
03:03 Challenges of Managing Machine Learning Projects
10:00 The Concept of Experiments in Machine Learning
12:51 Data Curation and Validation for High-Quality Data
27:07 Connecting the Components of Machine Learning Projects with DAGS Hub
29:12 The Importance of Data and Clear Interfaces
43:29 Incorporating Machine Learning into DAGsHub
51:27 The Future of ML and AI

]]> Fri, 27 Sep 2024 12:41:22 -0700 Kostas, Nitay Kostas, Nitay 3235 In this episode, we chat with Dean Pleban, CEO of DagsHub, about machine learning operations. We explore the differences between DevOps and MLOps, focusing on data management and experiment tracking. Dean shares insights on versioning various components in ML projects and discusses the importance of user experience in MLOps tools. We also touch on DagsHub's integration of AI in their product and Dean's vision for the future of AI and machine learning in industry.

Links

DagsHub
The MLOps Podcast
Dean on LI

Chapters

00:00 Introduction and Background
03:03 Challenges of Managing Machine Learning Projects
10:00 The Concept of Experiments in Machine Learning
12:51 Data Curation and Validation for High-Quality Data
27:07 Connecting the Components of Machine Learning Projects with DAGS Hub
29:12 The Importance of Data and Clear Interfaces
43:29 Incorporating Machine Learning into DAGsHub
51:27 The Future of ML and AI

]]> MLOps, LLMs, AI, ML Engineering, Data Engineering, Data Management No How Denormalized is Building ‘DuckDB for Streaming’ with Apache DataFusion 3 3 How Denormalized is Building ‘DuckDB for Streaming’ with Apache DataFusion full e05116c1-8b22-4036-a33e-a0131adcac6e https://techontherocks.show/3 In this episode, Kostas and Nitay are joined by Amey Chaugule and Matt Green, co-founders of Denormalized. They delve into how Denormalized is building an embedded stream processing engine—think “DuckDB for streaming”—to simplify real-time data workloads. Drawing from their extensive backgrounds at companies like Uber, Lyft, Stripe, and Coinbase. Amey and Matt discuss the challenges of existing stream processing systems like Spark, Flink, and Kafka. They explain how their approach leverages Apache DataFusion, to create a single-node solution that reduces the complexities inherent in distributed systems.


The conversation explores topics such as developer experience, fault tolerance, state management, and the future of stream processing interfaces. Whether you’re a data engineer, application developer, or simply interested in the evolution of real-time data infrastructure, this episode offers valuable insights into making stream processing more accessible and efficient.


Contacts & Links
Amey Chaugule
Matt Green
Denormalized
Denormalized Github Repo

Chapters
00:00 Introduction and Background
12:03 Building an Embedded Stream Processing Engine
18:39 The Need for Stream Processing in the Current Landscape
22:45 Interfaces for Interacting with Stream Processing Systems
26:58 The Target Persona for Stream Processing Systems
31:23 Simplifying Stream Processing Workloads and State Management
34:50 State and Buffer Management
37:03 Distributed Computing vs. Single-Node Systems
42:28 Cost Savings with Single-Node Systems
47:04 The Power and Extensibility of Data Fusion
55:26 Integrating Data Store with Data Fusion
57:02 The Future of Streaming Systems
01:00:18 intro-outro-fade.mp3

Click here to view the episode transcript.


]]> In this episode, Kostas and Nitay are joined by Amey Chaugule and Matt Green, co-founders of Denormalized. They delve into how Denormalized is building an embedded stream processing engine—think “DuckDB for streaming”—to simplify real-time data workloads. Drawing from their extensive backgrounds at companies like Uber, Lyft, Stripe, and Coinbase. Amey and Matt discuss the challenges of existing stream processing systems like Spark, Flink, and Kafka. They explain how their approach leverages Apache DataFusion, to create a single-node solution that reduces the complexities inherent in distributed systems.


The conversation explores topics such as developer experience, fault tolerance, state management, and the future of stream processing interfaces. Whether you’re a data engineer, application developer, or simply interested in the evolution of real-time data infrastructure, this episode offers valuable insights into making stream processing more accessible and efficient.


Contacts & Links
Amey Chaugule
Matt Green
Denormalized
Denormalized Github Repo

Chapters
00:00 Introduction and Background
12:03 Building an Embedded Stream Processing Engine
18:39 The Need for Stream Processing in the Current Landscape
22:45 Interfaces for Interacting with Stream Processing Systems
26:58 The Target Persona for Stream Processing Systems
31:23 Simplifying Stream Processing Workloads and State Management
34:50 State and Buffer Management
37:03 Distributed Computing vs. Single-Node Systems
42:28 Cost Savings with Single-Node Systems
47:04 The Power and Extensibility of Data Fusion
55:26 Integrating Data Store with Data Fusion
57:02 The Future of Streaming Systems
01:00:18 intro-outro-fade.mp3

Click here to view the episode transcript.


]]> Thu, 12 Sep 2024 20:16:47 -0700 Kostas, Nitay Kostas, Nitay 3721 In this episode, Kostas and Nitay are joined by Amey Chaugule and Matt Green, co-founders of Denormalized. They delve into how Denormalized is building an embedded stream processing engine—think “DuckDB for streaming”—to simplify real-time data workloads. Drawing from their extensive backgrounds at companies like Uber, Lyft, Stripe, and Coinbase. Amey and Matt discuss the challenges of existing stream processing systems like Spark, Flink, and Kafka. They explain how their approach leverages Apache DataFusion, to create a single-node solution that reduces the complexities inherent in distributed systems.


The conversation explores topics such as developer experience, fault tolerance, state management, and the future of stream processing interfaces. Whether you’re a data engineer, application developer, or simply interested in the evolution of real-time data infrastructure, this episode offers valuable insights into making stream processing more accessible and efficient.


Contacts & Links
Amey Chaugule
Matt Green
Denormalized
Denormalized Github Repo

Chapters
00:00 Introduction and Background
12:03 Building an Embedded Stream Processing Engine
18:39 The Need for Stream Processing in the Current Landscape
22:45 Interfaces for Interacting with Stream Processing Systems
26:58 The Target Persona for Stream Processing Systems
31:23 Simplifying Stream Processing Workloads and State Management
34:50 State and Buffer Management
37:03 Distributed Computing vs. Single-Node Systems
42:28 Cost Savings with Single-Node Systems
47:04 The Power and Extensibility of Data Fusion
55:26 Integrating Data Store with Data Fusion
57:02 The Future of Streaming Systems
01:00:18 intro-outro-fade.mp3

Click here to view the episode transcript.


]]> Stream Processing, Denormalized, Apache DataFusion, DuckDB, Real-time Data Infrastructure, Apache Arrow No Unifying structured and unstructured data for AI: Rethinking ML infrastructure with Nikhil Simha and Varant Zanoyan 2 2 Unifying structured and unstructured data for AI: Rethinking ML infrastructure with Nikhil Simha and Varant Zanoyan full f4307a63-c113-4b07-8ec0-646d1aeb3dbc https://techontherocks.show/2 In this episode, we dive deep into the future of data infrastructure for AI and ML with Nikhil Simha and Varant Zanoyan, two seasoned engineers from Airbnb and Facebook. Nikhil and Varant share their journey from building real-time data systems and ML infrastructure at tech giants to launching their own venture.

The conversation explores the intricacies of designing developer-friendly APIs, the complexities of handling both batch and streaming data, and the delicate balance between customer needs and product vision in a startup environment.

Contacts & Links

Nikhil Simha
Varant Zanoyan
Chronon project

Chapters

00:00 Introduction and Past Experiences
04:38 The Challenges of Building Data Infrastructure for Machine Learning
08:01 Merging Real-Time Data Processing with Machine Learning
14:08 Backfilling New Features in Data Infrastructure
20:57 Defining Failure in Data Infrastructure
26:45 The Choice Between SQL and Data Frame APIs
34:31 The Vision for Future Improvements
38:17 Introduction to Chrono and Open Source
43:29 The Future of Chrono: New Computation Paradigms
48:38 Balancing Customer Needs and Vision
57:21 Engaging with Customers and the Open Source Community
01:01:26 Potential Use Cases and Future Directions

Click here to view the episode transcript.

]]> In this episode, we dive deep into the future of data infrastructure for AI and ML with Nikhil Simha and Varant Zanoyan, two seasoned engineers from Airbnb and Facebook. Nikhil and Varant share their journey from building real-time data systems and ML infrastructure at tech giants to launching their own venture.

The conversation explores the intricacies of designing developer-friendly APIs, the complexities of handling both batch and streaming data, and the delicate balance between customer needs and product vision in a startup environment.

Contacts & Links

Nikhil Simha
Varant Zanoyan
Chronon project

Chapters

00:00 Introduction and Past Experiences
04:38 The Challenges of Building Data Infrastructure for Machine Learning
08:01 Merging Real-Time Data Processing with Machine Learning
14:08 Backfilling New Features in Data Infrastructure
20:57 Defining Failure in Data Infrastructure
26:45 The Choice Between SQL and Data Frame APIs
34:31 The Vision for Future Improvements
38:17 Introduction to Chrono and Open Source
43:29 The Future of Chrono: New Computation Paradigms
48:38 Balancing Customer Needs and Vision
57:21 Engaging with Customers and the Open Source Community
01:01:26 Potential Use Cases and Future Directions

Click here to view the episode transcript.

]]> Fri, 30 Aug 2024 15:53:21 -0700 Kostas, Nitay Kostas, Nitay 3705 In this episode, we dive deep into the future of data infrastructure for AI and ML with Nikhil Simha and Varant Zanoyan, two seasoned engineers from Airbnb and Facebook. Nikhil and Varant share their journey from building real-time data systems and ML infrastructure at tech giants to launching their own venture.

The conversation explores the intricacies of designing developer-friendly APIs, the complexities of handling both batch and streaming data, and the delicate balance between customer needs and product vision in a startup environment.

Contacts & Links

Nikhil Simha
Varant Zanoyan
Chronon project

Chapters

00:00 Introduction and Past Experiences
04:38 The Challenges of Building Data Infrastructure for Machine Learning
08:01 Merging Real-Time Data Processing with Machine Learning
14:08 Backfilling New Features in Data Infrastructure
20:57 Defining Failure in Data Infrastructure
26:45 The Choice Between SQL and Data Frame APIs
34:31 The Vision for Future Improvements
38:17 Introduction to Chrono and Open Source
43:29 The Future of Chrono: New Computation Paradigms
48:38 Balancing Customer Needs and Vision
57:21 Engaging with Customers and the Open Source Community
01:01:26 Potential Use Cases and Future Directions

Click here to view the episode transcript.

]]> technology, infrastructure, cloud, systems, data No Stream processing, LSMs and leaky abstractions with Chris Riccomini 1 1 Stream processing, LSMs and leaky abstractions with Chris Riccomini full ecd5d1f9-5475-4948-ade4-c77d6a84f876 https://techontherocks.show/1 In this episode, we  chat with Chris Riccomini about the evolution of stream processing and the challenges in building applications on streaming systems. We also chat about leaky abstractions, good and bad API designs, what Chris loves and hates about Rust and finally about his exciting new project that involves object storage and LSMs.
 
Connect with Chris at:
LinkedIn
X
Blog
Materialized View Newsletter - His newsletter
The missing README - His book
SlateDB - His latest OSS Project

Chapters
00:00 Introduction and Background

04:05 The State of Stream Processing Today

08:53 The Limitations of SQL in Streaming Systems

14:00 Prioritizing the Developer Experience in Stream Processing

18:15  Improving the Usability of Streaming Systems

27:54 The Potential of State Machine Programming in Complex Systems

32:41 The Power of Rust: Compiling and Language Bindings

34:06 The Shift from Sidecar to Embedded Libraries Driven by Rust

35:49 Building an LSM on Object Storage: Cost-Effective State Management

39:47 The Unbundling and Composable Nature of Databases

47:30 The Future of Data Systems: More Companies and Focus on Metadata


Click here to view the episode transcript.

]]> In this episode, we  chat with Chris Riccomini about the evolution of stream processing and the challenges in building applications on streaming systems. We also chat about leaky abstractions, good and bad API designs, what Chris loves and hates about Rust and finally about his exciting new project that involves object storage and LSMs.
 
Connect with Chris at:
LinkedIn
X
Blog
Materialized View Newsletter - His newsletter
The missing README - His book
SlateDB - His latest OSS Project

Chapters
00:00 Introduction and Background

04:05 The State of Stream Processing Today

08:53 The Limitations of SQL in Streaming Systems

14:00 Prioritizing the Developer Experience in Stream Processing

18:15  Improving the Usability of Streaming Systems

27:54 The Potential of State Machine Programming in Complex Systems

32:41 The Power of Rust: Compiling and Language Bindings

34:06 The Shift from Sidecar to Embedded Libraries Driven by Rust

35:49 Building an LSM on Object Storage: Cost-Effective State Management

39:47 The Unbundling and Composable Nature of Databases

47:30 The Future of Data Systems: More Companies and Focus on Metadata


Click here to view the episode transcript.

]]> Thu, 22 Aug 2024 22:28:56 -0700 Kostas, Nitay Kostas, Nitay 3186 In this episode, we  chat with Chris Riccomini about the evolution of stream processing and the challenges in building applications on streaming systems. We also chat about leaky abstractions, good and bad API designs, what Chris loves and hates about Rust and finally about his exciting new project that involves object storage and LSMs.
 
Connect with Chris at:
LinkedIn
X
Blog
Materialized View Newsletter - His newsletter
The missing README - His book
SlateDB - His latest OSS Project

Chapters
00:00 Introduction and Background

04:05 The State of Stream Processing Today

08:53 The Limitations of SQL in Streaming Systems

14:00 Prioritizing the Developer Experience in Stream Processing

18:15  Improving the Usability of Streaming Systems

27:54 The Potential of State Machine Programming in Complex Systems

32:41 The Power of Rust: Compiling and Language Bindings

34:06 The Shift from Sidecar to Embedded Libraries Driven by Rust

35:49 Building an LSM on Object Storage: Cost-Effective State Management

39:47 The Unbundling and Composable Nature of Databases

47:30 The Future of Data Systems: More Companies and Focus on Metadata


Click here to view the episode transcript.

]]> Keywords software engineering, infrastructure, data infrastructure, service meshes, streaming, stream processing, workflow systems, SQL, leaky abstraction, lower-level API, explicit coding, durable execution, usability, Fizbee language, Rust, formal proof tools, LSM on object storage, use cases, decomposing databases, unbundling databases, diversity in data ecosystem, metadata No