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, 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, 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

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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 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 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 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 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 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 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