Learning paths

Read in curriculum order. Numbers show sequence; this site does not track learning completion.

Data engineering foundations

Beginner

Follow data from its source through cleaning and ETL to an analytical model.

Be comfortable reading tables. For the Python exercise, have Python 3 and basic knowledge of functions.

Explain pipeline boundaries, clean transactions, build a small ETL job, and distinguish facts from dimensions.

Follow sample transactions from their source to a table ready for analysis.

Normalize missing identifiers, choose a deterministic duplicate winner, and reconcile rejected transactions.

Read a small CSV, reject invalid amounts explicitly, and write a repeatable JSON Lines output.

Choose a sales fact grain before joining descriptive dimensions and aggregating revenue.

Practitioner · Intermediate to advanced

Connect scheduling, transformation tests, and safe replay.

Finish Foundations or be comfortable with SQL, Python functions, ETL, and dimensional models.

Read DAG dependencies, choose model tests, and design idempotent writes for recovery.

Define task dependencies and bounded retries while keeping batch publication safe to repeat.

Connect a model with ref, test its business key, and review incremental correctness separately.

Use a business key and source revision to make a destination update safe to replay.