Data engineering foundations
Read in curriculum order. Numbers show sequence; this site does not track learning completion.
Follow data from its source through cleaning and ETL to an analytical model.
Prerequisites
Be comfortable reading tables. For the Python exercise, have Python 3 and basic knowledge of functions.
Outcomes
Explain pipeline boundaries, clean transactions, build a small ETL job, and distinguish facts from dimensions.
Reading order
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.
All learning paths · Streaming introduction (standalone reading)