Repo Mastery All sample repos
Sample onboarding breakdown

How to onboard to analytics-etl

acme/analytics-etl · Python

A Python ETL pipeline that extracts events from source systems, transforms them into a star schema, and loads them into the warehouse on a scheduled Airflow DAG.

A data pipeline where idempotent, re-runnable steps and clear schema contracts matter: a bad run should always be safe to replay.

PythonAirflowpandasdbtSnowflake

First files to read

The fastest way into analytics-etl: read these 5 files first, in order.

  1. 1dags/analytics_dag.pyAirflow DAG defining schedule and task order
  2. 2pipeline/extract.pyPulls raw events from source systems
  3. 3pipeline/transform.pyCleans and reshapes into the warehouse schema
  4. 4pipeline/load.pyIdempotent load into the warehouse
  5. 5schemas/warehouse.sqlTarget fact and dimension table definitions

Main systems

OrchestrationAirflow DAG scheduling and dependencies
ExtractSource-system connectors
TransformCleaning and reshaping records
LoadIdempotent warehouse writes

Key terms

ETL
Extract, Transform, Load: the three stages of moving data into a warehouse.
DAG
A directed acyclic graph of tasks that defines run order and dependencies.
Idempotent load
A load step safe to re-run without duplicating rows.
Star schema
A warehouse design with a central fact table joined to dimension tables.

Master any codebase this fast. Repo Mastery turns a repo into first files, architecture maps, flashcards, drills, and shareable proof.

Master your own repo

More sample repositories

acme-payments-apiatlas-weborders-serviceferro-clisaas-starterBrowse all