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Data Engineering Podcast

Tobias Macey

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

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Episodis

521 episodis

  1. Entity Resolution at Scale: Graphs, LLMs, and Real-World Data Messiness 58:50
  2. How Amazon Uses Telemetry and AI to Automate Physical Logistics 50:06
  3. Why Metadata Still Matters: From Data Catalogs to AI-Ready Semantic Hubs 1:24:52
  4. Reducing Data Debt with Agile Ledger Architecture 49:40
  5. What Context Really Means in Data Engineering and AI 52:47
  6. Specialized AI for Data Engineers: Inside Astronomer’s Otto 45:30
  7. Why Multi-Agent Systems Need Shared State, Graph Semantics, and Governance 1:02:15
  8. Building the Context Flywheel for AI Data Agents 1:00:34
  9. Holding Kafka Right: Product-Friendly Streaming with TypeStream 49:51
  10. Text to Data Products: Kaarvi’s End-to-End AI for Ingestion, Quality, and Dashboards 52:52
  11. Scaling Graph Analytics Without ETL: Inside PuppyGraph’s Architecture 54:20
  12. Maximizing GPU Utilization: Heterogeneous Pipelines with Ray and Kubernetes 58:34
  13. The AI-First Data Engineer: 10–50x Productivity and What Changes Next 59:24
  14. Treat Metering Like Finance: Building Data Platforms for Consumption Economics 50:19
  15. Beyond the PDF: Rowan Cockett on Reproducible, Composable Science 42:40
  16. Beyond Prompts: Practical Paths to Self‑Improving AI 1:01:50
  17. Orion at Gravity: Trustworthy AI Analysts for the Enterprise 1:05:01
  18. From Models to Momentum: Uniting Architects and Engineers with ER/Studio 45:02
  19. From Data Models to Mind Models: Designing AI Memory at Scale 57:47
  20. Prompt Management, Tracing, and Evals: The New Table Stakes for GenAI Ops 50:43
  21. From Legacy to AI-Ready: How MongoDB AMP Accelerates Modernization 46:45
  22. Branches, Diffs, and SQL: How Dolt Powers Agentic Workflows 56:53
  23. Logical First, Physical Second: A Pragmatic Path to Trusted Data 40:50
  24. Your Data, Your Lake: How Observe Uses Iceberg and Streaming ETL for Observability 1:12:21
  25. Semantic Operators Meet Dataframes: Building Context for Agents with FENIC 56:42
  26. Beyond Dashboards: How Data Teams Earn a Seat at the Table 49:21
  27. Unfreezing The Data Lake: The Future-Proof File Format 59:24
  28. From Context to Semantics: How Metadata Powers Agentic AI 1:06:17
  29. From Data Engineering to AI Engineering: Where the Lines Blur 26:59
  30. Malloy: Hierarchical Data, Semantic Models, and the Future of Analytics 58:48
  31. Blurring Lines: Data, AI, and the New Playbook for Team Velocity 1:00:57
  32. State, Scale, and Signals: Rethinking Orchestration with Durable Execution 51:46
  33. The AI Data Paradox: High Trust in Models, Low Trust in Data 51:35
  34. Bridging the AI–Data Gap: Collect, Curate, Serve 50:40
  35. Beyond the Perimeter: Practical Patterns for Fine‑Grained Data Access 1:05:00
  36. The True Costs of Legacy Systems: Technical Debt, Risk, and Exit Strategies 1:04:16
  37. Context Engineering as a Discipline: Building Governed AI Analytics 51:58
  38. The Data Model That Captures Your Business: Metric Trees Explained 1:01:05
  39. From GPUs-as-a-Service to Workloads-as-a-Service: Flex AI’s Path to High-Utilization AI Infra 56:31
  40. From RAG to Relational: How Agentic Patterns Are Reshaping Data Architecture 52:58
  41. Duck Lake: Simplifying the Lakehouse Ecosystem 1:10:41
  42. Aligning Business and Data: The Essential Role of Data Modeling 1:06:51
  43. From Academia to Industry: Bridging Data Engineering Challenges 50:54
  44. High Performance And Low Overhead Graphs With KuzuDB 1:01:29
  45. Bridging Data and Decision-Making: AI's Role in Modern Analytics 1:10:44
  46. From Bits to Tables: The Evolution of S3 Storage 50:08
  47. Revolutionizing Python Notebooks with Marimo 51:56
  48. Warehouse Native Incremental Data Processing With Dynamic Tables And Delayed View Semantics 55:07
  49. Streamlining Data Pipelines with MCP Servers and Vector Engines 52:04
  50. Foundational Data Engineering At Two Sigma 55:05