Day 42 of 45 · Pro lesson

Production Engineering & CI/CD

The curriculum for this day stays visible. The lesson, code, and workspace unlock with Pro.

Teaches: Git trunk-based development & feature branching, Data pipeline CI/CD with automated testing stages, Docker containerization for reproducible Spark/Airflow environments, Secrets management & credential rotation (IAM, Vault, AWS Secrets Manager), Infrastructure as Code (Terraform) principles, Observability: Metrics, logs, traces, and SLO/SLA error budgets

Build: Production CI/CD & validation pipeline: GitHub Actions simulator → linting → SQL fluff → PySpark unit tests → Docker build → staging deployment gate.

•Complete 15 drills on Git workflows, Dockerfile multi-stage builds, secrets rotation, and error budget calculations.
•Run 6 Python modules simulating CI test runners, container health checks, and secret masking.
•Simulate 5 production failures: leaked AWS key in git commit, failed deployment rollback, Docker image drift, and breached error budget.
•Defend 10 senior production engineering questions and complete the blank-page exam.

Never test data pipelines in production. Enforce automated pre-merge testing with mock datasets to catch bugs before they corrupt production tables.

This section walks through the idea with a short example, then the trade-offs you should mention in an interview.

In practice you start from the raw rows, apply the transform step by step, and check the shape of the result before you move on.

A common mistake is to jump straight to the final query without naming the grain or the join keys that keep the result correct.

Once the core path works, you harden it for nulls, duplicates, and late data so the pipeline stays reliable under load.

The Pro write-up covers the full explanation, worked examples, and the code you can run in the studio.

# Locked example
result = transform(frame)
print(result.head())

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