Data Engineering Podcast
Podcast tekijän mukaan Tobias Macey - Sunnuntaisin

Kategoriat:
419 Jaksot
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Leveraging Human Intelligence For Better AI At Alegion With Cheryl Martin - Episode 38
Julkaistiin: 2.7.2018 -
Package Management And Distribution For Your Data Using Quilt with Kevin Moore - Episode 37
Julkaistiin: 25.6.2018 -
User Analytics In Depth At Heap with Dan Robinson - Episode 36
Julkaistiin: 17.6.2018 -
CockroachDB In Depth with Peter Mattis - Episode 35
Julkaistiin: 11.6.2018 -
ArangoDB: Fast, Scalable, and Multi-Model Data Storage with Jan Steeman and Jan Stücke - Episode 34
Julkaistiin: 4.6.2018 -
The Alooma Data Pipeline With CTO Yair Weinberger - Episode 33
Julkaistiin: 28.5.2018 -
PrestoDB and Starburst Data with Kamil Bajda-Pawlikowski - Episode 32
Julkaistiin: 21.5.2018 -
Brief Conversations From The Open Data Science Conference: Part 2 - Episode 31
Julkaistiin: 14.5.2018 -
Brief Conversations From The Open Data Science Conference: Part 1 - Episode 30
Julkaistiin: 7.5.2018 -
Metabase Self Service Business Intelligence with Sameer Al-Sakran - Episode 29
Julkaistiin: 30.4.2018 -
Octopai: Metadata Management for Better Business Intelligence with Amnon Drori - Episode 28
Julkaistiin: 23.4.2018 -
Data Engineering Weekly with Joe Crobak - Episode 27
Julkaistiin: 15.4.2018 -
Defining DataOps with Chris Bergh - Episode 26
Julkaistiin: 8.4.2018 -
ThreatStack: Data Driven Cloud Security with Pete Cheslock and Patrick Cable - Episode 25
Julkaistiin: 1.4.2018 -
MarketStore: Managing Timeseries Financial Data with Hitoshi Harada and Christopher Ryan - Episode 24
Julkaistiin: 25.3.2018 -
Stretching The Elastic Stack with Philipp Krenn - Episode 23
Julkaistiin: 19.3.2018 -
Database Refactoring Patterns with Pramod Sadalage - Episode 22
Julkaistiin: 12.3.2018 -
The Future Data Economy with Roger Chen - Episode 21
Julkaistiin: 5.3.2018 -
Honeycomb Data Infrastructure with Sam Stokes - Episode 20
Julkaistiin: 26.2.2018 -
Data Teams with Will McGinnis - Episode 19
Julkaistiin: 19.2.2018
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.