DataOps brings DevOps principles to data engineering. This 30-day roadmap will help you implement version control, testing, CI/CD, and monitoring for your data pipelines.
Week 1: Version Control Everything
Move all SQL, ETL scripts, and data transformation code into Git. Document your data models. Create a data dictionary. Establish branching strategies and code review processes for data changes.
Week 2: Automated Testing
Implement data quality tests: schema validation, null checks, range validation, and referential integrity. Create unit tests for transformation logic. Set up automated test runs on every commit.
Week 3: CI/CD for Data
Build deployment pipelines for data infrastructure. Automate schema migrations. Implement blue-green deployments for breaking changes. Create rollback procedures for failed deployments.
Week 4: Observability and Monitoring
Instrument your data pipelines with logging and metrics. Set up alerts for data quality issues, pipeline failures, and SLA violations. Create dashboards showing data freshness, volume trends, and error rates.
Conclusion
DataOps is a journey, not a destination. These 30 days lay the foundation. Continue iterating on your processes, expanding test coverage, and improving observability to build truly reliable data systems.
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