CICD Process For Spark Batch Data Pipeline On AWS
Why It Matters
Automating Spark job CI/CD on AWS accelerates delivery and minimizes deployment errors, giving data engineering teams a reliable, scalable path to production.
Key Takeaways
- •CI runs linting, formatting, and Spark unit tests automatically
- •GitHub Actions orchestrate build, test, and artifact creation pipeline
- •Artifacts are stored in S3 before deployment to EMR or Glue
- •Separate dev, staging, and prod environments ensure safe incremental releases
- •Pull request merges trigger production deployment of the same artifact
Summary
The video outlines a complete CI/CD workflow for Spark batch data pipelines running on AWS, detailing how code moves from a developer’s machine to production clusters such as EMR or Glue.
In the continuous‑integration stage, GitHub Actions automatically run linting, formatting checks, unit tests, and code‑coverage analysis, then compile the code with SBT or pip to produce a deployable artifact. The continuous‑deployment stage uploads that artifact to an S3 bucket and triggers its deployment to a development EMR/Glue cluster.
When the code passes dev validation, the same artifact is promoted to a staging environment where integration tests run; a pull‑request merge then promotes the artifact to production without rebuilding. The presenter notes that a PDF of the pipeline diagram will be sent via direct message.
By automating testing and deployment across isolated environments, the process reduces manual errors, shortens release cycles, and gives data engineering teams a reproducible, version‑controlled path to push Spark jobs into production at scale.
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