Data Engineering Podcast

A podcast by Tobias Macey - Duminică

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419 Episoade

  1. Building And Managing Data Teams And Data Platforms In Large Organizations With Ashish Mrig

    Publicat: 23.01.2022
  2. Automated Data Quality Management Through Machine Learning With Anomalo

    Publicat: 15.01.2022
  3. An Introduction To Data And Analytics Engineering For Non-Programmers

    Publicat: 15.01.2022
  4. Open Source Reverse ETL For Everyone With Grouparoo

    Publicat: 08.01.2022
  5. Data Observability Out Of The Box With Metaplane

    Publicat: 08.01.2022
  6. Creating Shared Context For Your Data Warehouse With A Controlled Vocabulary

    Publicat: 02.01.2022
  7. A Reflection On The Data Ecosystem For The Year 2021

    Publicat: 02.01.2022
  8. Revisiting The Technical And Social Benefits Of The Data Mesh

    Publicat: 27.12.2021
  9. Exploring The Evolving Role Of Data Engineers

    Publicat: 27.12.2021
  10. Fast And Flexible Headless Data Analytics With Cube.JS

    Publicat: 21.12.2021
  11. Building A System Of Record For Your Organization's Data Ecosystem At Metaphor

    Publicat: 20.12.2021
  12. Building Auditable Spark Pipelines At Capital One

    Publicat: 13.12.2021
  13. Deliver Personal Experiences In Your Applications With The Unomi Open Source Customer Data Platform

    Publicat: 12.12.2021
  14. Data Driven Hiring For Data Professionals With Alooba

    Publicat: 04.12.2021
  15. Experimentation and A/B Testing For Modern Data Teams With Eppo

    Publicat: 04.12.2021
  16. Creating A Unified Experience For The Modern Data Stack At Mozart Data

    Publicat: 27.11.2021
  17. Doing DataOps For External Data Sources As A Service at Demyst

    Publicat: 27.11.2021
  18. Exploring Processing Patterns For Streaming Data Integration In Your Data Lake

    Publicat: 20.11.2021
  19. Laying The Foundation Of Your Data Platform For The Era Of Big Complexity With Dagster

    Publicat: 20.11.2021
  20. Data Quality Starts At The Source

    Publicat: 14.11.2021

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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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