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Overview
This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering.
News
Why Multi-Agent Systems Need Shared State, Graph Semantics, and Governance
Summary In this episode Ragnor Comerford talks about OmniGraph, a lakehouse-native graph storage layer designed around the needs of agentic systems. He explores how graphs are primarily a semantic model for representing the world, rather than just a speci
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Building the Context Flywheel for AI Data Agents
Summary In this episode Prukalpa Sankar, co-founder of Atlan, talks about what it takes to build a "context flywheel" for AI agents in data-intensive organizations. She explained why model intelligence alone isn't enough to make AI useful in production, a
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Holding Kafka Right: Product-Friendly Streaming with TypeStream
Summary In this episode Jevin Maltais talks about the practical realities of building reliable, product-focused streaming systems with Kafka. Jevin shares lessons from roles at Zapier, Humi, and Clio, where real-time synchronization, customer data unifica
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Text to Data Products: Kaarvi's End-to-End AI for Ingestion, Quality, and Dashboards
Summary In this episode Shravan Gunda, founder and CEO of Kaarvi AI, talks about building an AI-native, agent-driven data platform designed to eliminate the janitorial work that consumes most data teams. He explores Kaarvi's multi-agent architecture that
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Scaling Graph Analytics Without ETL: Inside PuppyGraph's Architecture
SummaryIn this episode Weimo Liu, co-founder of PuppyGraph, talks about the engineering behind their "zero-copy" graph querying engine for lakehouse and database sources. He explores how PuppyGraph lets you run Cypher and Gremlin traversals and graph algo
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