Day 38 of 45 · Pro lesson

Project #1: Airflow, dbt & SLA Defense

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

Teaches: End-to-end Airflow DAG tying extraction, Spark, and dbt together, Warehouse atomic MERGE / COPY ingestion gates, dbt data tests as blocking deployment gates, SLA monitoring & PagerDuty alert routing, Complete system architecture verbal defense, Cost & performance trade-off justification

Build: Platform Stage 3: End-to-end Airflow master DAG orchestrating extraction, EMR Spark, Redshift COPY, dbt tests, and Slack/PagerDuty webhook alerts.

•Complete 15 drills on master DAG dependencies, warehouse copy gates, blocking dbt test triggers, and SLA triage.
•Run 6 Python modules simulating end-to-end pipeline execution, reconciliation auditing, and alert dispatch.
•Simulate 5 production failures: warehouse lock timeout, dbt test rejection, SLA breach alert, and partial pipeline recovery.
•Deliver the 20-minute architecture defense presentation and complete the full blank-page architecture blueprint.

In a Senior DE interview, walk through this platform using the 'Requirements → Bottlenecks → Trade-offs → Failure Modes' framework.

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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