Data Engineering Podcast

A podcast by Tobias Macey - Domenica

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419 Episodio

  1. Navigating Boundless Data Streams With The Swim Kernel - Episode 98

    Pubblicato: 18/09/2019
  2. Building A Reliable And Performant Router For Observability Data - Episode 97

    Pubblicato: 10/09/2019
  3. Building A Community For Data Professionals at Data Council - Episode 96

    Pubblicato: 02/09/2019
  4. Building Tools And Platforms For Data Analytics - Episode 95

    Pubblicato: 26/08/2019
  5. A High Performance Platform For The Full Big Data Lifecycle - Episode 94

    Pubblicato: 19/08/2019
  6. Digging Into Data Replication At Fivetran - Episode 93

    Pubblicato: 12/08/2019
  7. Solving Data Discovery At Lyft - Episode 92

    Pubblicato: 05/08/2019
  8. Simplifying Data Integration Through Eventual Connectivity - Episode 91

    Pubblicato: 29/07/2019
  9. Straining Your Data Lake Through A Data Mesh - Episode 90

    Pubblicato: 22/07/2019
  10. Data Labeling That You Can Feel Good About - Episode 89

    Pubblicato: 15/07/2019
  11. Scale Your Analytics On The Clickhouse Data Warehouse - Episode 88

    Pubblicato: 08/07/2019
  12. Stress Testing Kafka And Cassandra For Real-Time Anomaly Detection - Episode 87

    Pubblicato: 02/07/2019
  13. The Workflow Engine For Data Engineers And Data Scientists - Episode 86

    Pubblicato: 25/06/2019
  14. Maintaining Your Data Lake At Scale With Spark - Episode 85

    Pubblicato: 17/06/2019
  15. Managing The Machine Learning Lifecycle - Episode 84

    Pubblicato: 10/06/2019
  16. Evolving An ETL Pipeline For Better Productivity - Episode 83

    Pubblicato: 04/06/2019
  17. Data Lineage For Your Pipelines - Episode 82

    Pubblicato: 27/05/2019
  18. Build Your Data Analytics Like An Engineer - Episode 81

    Pubblicato: 20/05/2019
  19. Using FoundationDB As The Bedrock For Your Distributed Systems - Episode 80

    Pubblicato: 07/05/2019
  20. Running Your Database On Kubernetes With KubeDB - Episode 79

    Pubblicato: 29/04/2019

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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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