From DevOps for data to DataOps
summary
Argues DataOps extends database DevOps by adding continuous data testing, monitoring and governance, not just CI/CD with Git and Azure DevOps; claims reuse, metadata cataloguing, lineage and owners cut firefighting and free time for features. Recommends specific tests: ingestion (frequency, schema, statistical drift), transformation (row counts, types), and data quality; gives an ice‑cream sales example where bad source values (rain = -1) broke analyses. Roadmap is scan, educate, pilot, productize and stresses innovation pipeline vs value pipeline testing and platform automation (Power BI/Fabric, alerts, AI tagging).
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chapters (10)
- 0:00Introduction and agenda
- 2:37DevOps versus DataOps
- 6:02Addressing data challenges
- 14:29Testing innovation and value
- 17:43Data quality and testing
- 21:19Governance and metadata
- 29:05Organizational roles
- 32:00DataOps implementation roadmap
- 37:54Scaling and automation
- 44:31Wrap up and final thoughts
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