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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