Level 1 · Entry Foundations

Goal: understand every moving part of an ETL pipeline and the data lake it feeds — extraction, transformation, loading, file formats, layering, and why you need a scheduler — and ship a working ETL pipeline that lands data into a bronze/silver/gold lake, built from those parts in plain Python.

Modules

1. What Is ETL vs. ELT?
2. Data Sources & Ingestion Patterns
3. Extraction Basics (Files, APIs, Databases)
4. Transformation Basics (Clean, Cast, Dedupe)
5. Loading Into a Target
6. What Is a Data Lake? (vs. Data Warehouse)
7. Bronze/Silver/Gold Layering
8. File Formats: CSV, JSON, Parquet, Avro
9. Why You Need a Scheduler
10. Capstone — End-to-End ETL to a Bronze/Silver/Gold Lake

By the end of this level you'll be able to take a folder of raw files, run them through an extract → transform → load pipeline, and organize the result into layered zones on disk the way a real data lake does — recognizing the tradeoffs between CSV, JSON, and Parquet along the way.

Setup for this level

pip install pandas pyarrow

Everything else — csv, json, pathlib, sqlite3 — is in the Python standard library. No cloud account, no API key, no server to install. All code on this level runs fully offline.