DevDataOps - Data-Driven Product Development for Software Engineers
summary
The talk explains how a product team at Breuninger replaced gut-feel decisions with a data-driven DevDataOps approach to evaluate features faster and only build those that add real value. It covers practical examples of centralizing data from multiple sources for software engineers, rapid prototyping of feature hypotheses within days, and argues that owning your data is crucial for progressing toward a Data Mesh.
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chapters (14)
- 0:00Welcome
- 1:14Why data matters
- 3:03Usable distributed data
- 5:11Monolith to vertical architecture
- 6:13Postgres and Change Streams
- 7:36Why ML failed
- 8:39Horizontal team problems
- 10:26DevDataOps: data responsibility
- 11:31BigQuery was right
- 12:54Events in BigQuery
- 14:00Hypothesis to feature
- 15:49Package summary feature
- 16:33Data work challenges
- 17:56Summary
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