Making Database Changes Tier One Artifact
summary
Argues that database changes become deployable artifacts by storing versioned change sets in JFrog Artifactory and orchestrating them with Datical DB, pulled through CI (Jenkins plugin) and Application Release Automation. Shows how incremental scripts fail for upgrades/rollbacks and how Datical converts SQL, schema deltas and wizards into reproducible artifacts, enforces standards (indexes, data types, no dangerous SQL) and uses Datical DB forecast to predict environment compatibility. Claims this combo removes ticket delays, enables automated deploys and works at scale for large customers.
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chapters (9)
- 0:00Introduction and setup
- 2:32Database deployment problem
- 7:11Why DevOps matters now
- 12:28Code in production is done
- 15:23Aligning database changes with code
- 22:27Database standards and trust
- 25:39Expand migrate contract pattern
- 26:45Bringing DBAs into DevOps
- 27:47Q&A and closing
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