440 Episodes

  1. 95: How the Metrics Layer Bridges the Gap Between Data & Business with Nick Handel of Transform

    Published: 7/13/2022
  2. The PRQL: Data Marts Aren’t Just for the Enterprise

    Published: 7/8/2022
  3. 94: Notebooks Aren’t Just for Data Scientists With Barry McCardel of Hex Technologies

    Published: 7/6/2022
  4. The PRQL: Have You Ever Been a Part of a Company That Has Done Analytics Really Well?

    Published: 7/1/2022
  5. 93: There Is No Data Observability Without Lineage with Kevin Hu of Metaplane

    Published: 6/29/2022
  6. The PRQL: What Are the Similarities Between VCs and Tilapia?

    Published: 6/24/2022
  7. 92: Building a Decentralized Storage System for Media File Collaboration with Tejas Chopra

    Published: 6/22/2022
  8. The PRQL: What is Netflix Cloud?

    Published: 6/17/2022
  9. 91: The Future of Streaming Data with Stripe, Deephaven, Materialize, and Benthos

    Published: 6/15/2022
  10. The PRQL: Can Streaming Simplify Your Data Flows?

    Published: 6/10/2022
  11. 90: The Modern Data Stack Has a Join Problem with Ahmed Elsamadisi of Narrator AI

    Published: 6/8/2022
  12. The PRQL: Can One Table Rule Them All?

    Published: 6/3/2022
  13. 89: Solving Microservice Orchestration Issues at Netflix with Viren Baraiya of Orkes

    Published: 6/1/2022
  14. The PRQL: What are the Different Flavors of Orchestration?

    Published: 5/27/2022
  15. 88: What Is Data Observability? With Tristan Spaulding of Acceldata

    Published: 5/25/2022
  16. The PRQL: Does Data Exist if We Do Not Observe It?

    Published: 5/20/2022
  17. 87: Why Is Now the Golden Age of Data Analytics? With Cindi Howson of ThoughtSpot

    Published: 5/18/2022
  18. The PRQL: Can You Trust AI Enabled Analytics?

    Published: 5/13/2022
  19. 86: Solving the Data Quality Problem with Bigeye, Great Expectations, Metaplane, and Lightup.ai

    Published: 5/11/2022
  20. 85: You Can Stop Doing Data Fire Drills with Barr Moses of Monte Carlo

    Published: 5/4/2022

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Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.