Data Engineering Podcast
Podcast autorstwa Tobias Macey - Niedziele
Kategorie:
419 Odcinki
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The Benefits And Challenges Of Building A Data Trust - Episode 118
Opublikowany: 3.02.2020 -
Pay Down Technical Debt In Your Data Pipeline With Great Expectations - Episode 117
Opublikowany: 27.01.2020 -
Replatforming Production Dataflows - Episode 116
Opublikowany: 20.01.2020 -
Planet Scale SQL For The New Generation Of Applications - Episode 115
Opublikowany: 13.01.2020 -
Change Data Capture For All Of Your Databases With Debezium - Episode 114
Opublikowany: 6.01.2020 -
Building The DataDog Platform For Processing Timeseries Data At Massive Scale - Episode 113
Opublikowany: 30.12.2019 -
Building The Materialize Engine For Interactive Streaming Analytics In SQL - Episode 112
Opublikowany: 23.12.2019 -
Solving Data Lineage Tracking And Data Discovery At WeWork - Episode 111
Opublikowany: 16.12.2019 -
SnowflakeDB: The Data Warehouse Built For The Cloud - Episode 110
Opublikowany: 9.12.2019 -
Organizing And Empowering Data Engineers At Citadel - Episode 109
Opublikowany: 3.12.2019 -
Building A Real Time Event Data Warehouse For Sentry - Episode 108
Opublikowany: 26.11.2019 -
Escaping Analysis Paralysis For Your Data Platform With Data Virtualization - Episode 107
Opublikowany: 18.11.2019 -
Designing For Data Protection - Episode 106
Opublikowany: 11.11.2019 -
Automating Your Production Dataflows On Spark - Episode 105
Opublikowany: 4.11.2019 -
Build Maintainable And Testable Data Applications With Dagster - Episode 104
Opublikowany: 28.10.2019 -
Data Orchestration For Hybrid Cloud Analytics - Episode 103
Opublikowany: 22.10.2019 -
Keeping Your Data Warehouse In Order - Episode 102
Opublikowany: 15.10.2019 -
Fast Analytics On Semi-Structured And Structured Data In The Cloud - Episode 101
Opublikowany: 8.10.2019 -
Ship Faster With An Opinionated Data Pipeline Framework - Episode 100
Opublikowany: 1.10.2019 -
Open Source Object Storage For All Of Your Data - Episode 99
Opublikowany: 23.09.2019
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.