Data Engineering Podcast

En podkast av Tobias Macey - Søndager

Søndager

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419 Episoder

  1. Building A Cost Effective Data Catalog With Tree Schema

    Publisert: 10.11.2020
  2. Add Version Control To Your Data Lake With LakeFS

    Publisert: 3.11.2020
  3. Cloud Native Data Security As Code With Cyral

    Publisert: 26.10.2020
  4. Better Data Quality Through Observability With Monte Carlo

    Publisert: 19.10.2020
  5. Rapid Delivery Of Business Intelligence Using Power BI

    Publisert: 12.10.2020
  6. Self Service Real Time Data Integration Without The Headaches With Meroxa

    Publisert: 5.10.2020
  7. Speed Up And Simplify Your Streaming Data Workloads With Red Panda

    Publisert: 29.9.2020
  8. Cutting Through The Noise And Focusing On The Fundamentals Of Data Engineering With The Data Janitor

    Publisert: 22.9.2020
  9. Distributed In Memory Processing And Streaming With Hazelcast

    Publisert: 15.9.2020
  10. Simplify Your Data Architecture With The Presto Distributed SQL Engine

    Publisert: 7.9.2020
  11. Building A Better Data Warehouse For The Cloud At Firebolt

    Publisert: 1.9.2020
  12. Metadata Management And Integration At LinkedIn With DataHub

    Publisert: 25.8.2020
  13. Exploring The TileDB Universal Data Engine

    Publisert: 17.8.2020
  14. Closing The Loop On Event Data Collection With Iteratively

    Publisert: 10.8.2020
  15. A Practical Introduction To Graph Data Applications

    Publisert: 4.8.2020
  16. Build More Reliable Distributed Systems By Breaking Them With Jepsen

    Publisert: 28.7.2020
  17. Making Wind Energy More Efficient With Data At Turbit Systems

    Publisert: 21.7.2020
  18. Open Source Production Grade Data Integration With Meltano

    Publisert: 13.7.2020
  19. DataOps For Streaming Systems With Lenses.io

    Publisert: 6.7.2020
  20. Data Collection And Management For Teaching Machines To Hear At Audio Analytic - Episode 139

    Publisert: 30.6.2020

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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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