Day 13 of 45 · Pro lesson

ETL vs ELT, Batch vs Streaming, and Modern Data Pipelines

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

Teaches: ETL vs ELT, batch vs streaming, micro-batching, medallion stages, raw immutable storage, replay recovery

Build: Multi-source ingestion → immutable partitioned raw storage → dual ETL vs ELT comparison → analytics datamart.

•Complete 15 drills on ETL vs ELT decisions, Hive partitioning, tumbling windows, and replayability.
•Inspect and run 8 pipeline modules comparing Python ETL to SQL ELT execution.
•Simulate 5 production failures: warehouse corruption, silent row drops, schema drift, and rate limits.
•Defend 10 senior architecture interview questions and complete the blank-page review.

System trade-offs: compute costs of pre-load transforms vs in-warehouse SQL, and raw storage as replay insurance.

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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