How to Implement DataOps using
summary
Shows how GitLab implements DataOps end-to-end: version control + merge requests that trigger CI pipelines which clone the Snowflake warehouse per MR and run DBT for the T in ELT to run full refreshes, incremental builds, and data quality tests. Builds disposable, production-like test environments, enforces MR-based change management and documentation via DBT Docs, and surfaces regressions before merge. Result: fewer firefights over spreadsheets, reproducible metrics stored as code, and a clearer path from reporting to insights.
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chapters (7)
- 0:00Introduction to Data Ops
- 4:32What is Data Ops?
- 7:57Data stack analogy
- 10:13Data Ops implementation
- 14:03Using dbt and testing
- 21:16Data team maturity
- 22:36Q&A session
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