Tech on the Rocks

SummaryIn 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.Chapters00:00 From PHP to Kernel Development: A Journey07:30 Introducing Epsio: The Incremental Views Engine10:56 The Importance of Materialized Views15:07 Understanding Incremental Materialization19:21 Optimizing Query Performance with Epsio24:53 Integrating Epsio with Existing Databases27:02 The Shift from Theory to Practice in Data Processing29:42 Seamless Integration with Existing Databases32:02 Understanding Epsio Incremental Processing Mechanism34:46 Challenges and Limitations of Incremental Views36:49 The Complexity of Implementing Operators39:56 Trade-offs in Incremental Computation41:21 User Interaction with Epsio43:01 Comparing EPSIO with Streaming Systems45:09 Architectural Guarantees of Epsio50:33 The Future of Incremental Data Processing

Viktor Kessler, co-founder of Lakekeeper and former solutions architect at MongoDB and Dremio, discusses his journey through data management. He explains the shift from data mesh concepts to lake house architecture, highlighting how it aligns technical solutions with business needs. Viktor dives into the role of actionable metadata and the evolution of cataloging systems. He emphasizes the importance of a centralized approach in decentralized environments and shares ways for listeners to engage with the Lakekeeper community.

In this installment, Apurva Mehta, co-founder and CEO of Responsive, shares insights from his journey in stream processing at LinkedIn and Confluent. He breaks down the evolution of stream processing from simple tasks to powering complex applications. Apurva clarifies the concept of 'real time,' emphasizing low latency over instant responses. He discusses the pitfalls of traditional databases in handling high-update rates and explains how Responsive innovates by decoupling state from compute to enhance efficiency and operational simplicity.

David Jayatillake, VP of AI at Cube, shares his expertise on the critical role of semantic layers in bridging raw data and actionable insights. He reveals how Cube achieved 100% accuracy in natural language data queries using semantic layers, vastly outperforming traditional methods. The conversation dives into the challenges of building these layers, emphasizing the significance of clear naming and documentation. Jayatillake also discusses future trends, including AI-powered features set to launch in 2025, and the potential of LLMs as intelligent agents.

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 Healthcare04:50 The Complexity of Einstein's Equations and Their Solutions10:12 AI in Mathematics: The Ramanujan Machine and Conjectures15:41 Navigating Criticism: The Scientific Community's Response to Innovation29:24 The Impact of Algorithms in Mathematics35:30 The Planck Machine: A New Approach41:15 Neutrino: A Personal Journey in Nutrition50:11 Connecting Food Complexity to Health Metrics

Philippe Noël, founder of ParadeDB and an expert in PostgreSQL, discusses his venture to create a native search engine alternative to Elasticsearch. He explores the challenges of integrating full-text search and analytics within PostgreSQL. The conversation touches on the benefits of the bring-your-own-cloud model and the evolution of database extensions. Philippe emphasizes the rising demand for traditional search capabilities amidst AI advancements and addresses misconceptions about PostgreSQL's scalability and the trade-offs in cloud architectures.

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.Chapters00:00 Ben's Journey: From Startups to Big Tech13:00 The Importance of Timing in Entrepreneurship19:22 Consulting Insights: Learning from Clients23:32 Transitioning to Big Tech: Experiences at Uber and Google30:58 The Future of AI: End-to-End Systems and Data Utilization35:53 Transitioning Between Domains: From ML to Distributed Systems44:24 Espresso's Mission: Optimizing SQL with ML51:26 The Future of Code Optimization and AIClick here to view the episode transcript.

David Mytton, founder and CEO of Arcjet, shares his journey from cloud monitoring to creating developer-first security tools. He dives into the challenges of bot detection and the failures of traditional security methods. Mytton discusses using WebAssembly for rapid security checks and highlights the balance between security and latency. He also touches on the importance of documentation in developer tools and his work on sustainability in cloud computing, revealing how environmental impacts shape modern tech solutions.

Delve into the evolution of developer environments with insights from a pioneer in browser-based IDEs. Discover the challenges of integrating complex systems and the distinction between user needs and buyer demands. Explore the impact of AI on coding practices and the unique approach of standardization and automation. Ivan shares his experience transitioning to open source and the future of integrated development environments. Plus, hear about the dynamics of organizing tech conferences and the importance of adapting to emerging trends.

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.Chapters00:00 Introduction to Roy Benalta and AI Background04:07 Warren Buffett Experience and MBA Insights06:45 Lessons from Amazon and Meta Leadership09:15 Early Days of AWS and Cloud Adoption12:12 Redshift vs. Snowflake: A Data Warehouse Perspective14:49 Navigating Complex Data Systems in Organizations31:21 The Future of Personalized Software Solutions32:19 Building Large Language Models at Meta39:27 Evolution of Data Platforms and Infrastructure50:50 Engineering Knowledge and LLMs58:27 Build vs. Buy: Strategic Decisions for Startups