Data Engineering Podcast
En podcast av Tobias Macey - Söndagar
Kategorier:
419 Avsnitt
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The Benefits And Challenges Of Building A Data Trust - Episode 118
Publicerades: 2020-02-03 -
Pay Down Technical Debt In Your Data Pipeline With Great Expectations - Episode 117
Publicerades: 2020-01-27 -
Replatforming Production Dataflows - Episode 116
Publicerades: 2020-01-20 -
Planet Scale SQL For The New Generation Of Applications - Episode 115
Publicerades: 2020-01-13 -
Change Data Capture For All Of Your Databases With Debezium - Episode 114
Publicerades: 2020-01-06 -
Building The DataDog Platform For Processing Timeseries Data At Massive Scale - Episode 113
Publicerades: 2019-12-30 -
Building The Materialize Engine For Interactive Streaming Analytics In SQL - Episode 112
Publicerades: 2019-12-23 -
Solving Data Lineage Tracking And Data Discovery At WeWork - Episode 111
Publicerades: 2019-12-16 -
SnowflakeDB: The Data Warehouse Built For The Cloud - Episode 110
Publicerades: 2019-12-09 -
Organizing And Empowering Data Engineers At Citadel - Episode 109
Publicerades: 2019-12-03 -
Building A Real Time Event Data Warehouse For Sentry - Episode 108
Publicerades: 2019-11-26 -
Escaping Analysis Paralysis For Your Data Platform With Data Virtualization - Episode 107
Publicerades: 2019-11-18 -
Designing For Data Protection - Episode 106
Publicerades: 2019-11-11 -
Automating Your Production Dataflows On Spark - Episode 105
Publicerades: 2019-11-04 -
Build Maintainable And Testable Data Applications With Dagster - Episode 104
Publicerades: 2019-10-28 -
Data Orchestration For Hybrid Cloud Analytics - Episode 103
Publicerades: 2019-10-22 -
Keeping Your Data Warehouse In Order - Episode 102
Publicerades: 2019-10-15 -
Fast Analytics On Semi-Structured And Structured Data In The Cloud - Episode 101
Publicerades: 2019-10-08 -
Ship Faster With An Opinionated Data Pipeline Framework - Episode 100
Publicerades: 2019-10-01 -
Open Source Object Storage For All Of Your Data - Episode 99
Publicerades: 2019-09-23
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.