ETL & Data Lake Mastery Path¶

Mastery Path

BOOTCAMP

VINOD BHANUJA

A structured, module-wise training program on ETL/ELT pipeline design and Data Lake architecture — from your first extract-transform-load script to governed, multi-zone lakehouse platforms — with runnable Python code in every module and a hands-on project at the end of each level.

ETL is the plumbing behind every analytics dashboard, ML feature store, and finance report: moving data reliably from where it's produced to where it's trusted and queried. A Data Lake is where a huge share of that data ends up living. This site teaches both from first principles — plain Python and file formats first, frameworks and lakehouse table formats (Airflow, Delta Lake/Iceberg/Hudi) once you understand what problem they actually solve.

How the program is organized¶

Level	Focus	Modules
Level 1 · Entry	ETL vs. ELT, ingestion patterns, extraction/transformation/loading basics, what a data lake is, bronze/silver/gold layering, file formats, orchestration concept	9 topics + 1 capstone
Level 2 · Intermediate	Incremental loads & CDC, data quality checks, partitioning, schema evolution, orchestration tools (Airflow), idempotent design, backfills, monitoring, data contracts	9 topics + 1 project
Level 3 · Advanced	Lakehouse concepts (Delta/Iceberg/Hudi), streaming ETL, cataloging & metadata, cost/performance optimization, late-arriving data, compaction, time travel, ACID on the lake	9 topics + 1 project
Level 4 · Master	Governed multi-zone lake architecture, lineage & observability, security & access control, lake vs. warehouse vs. lakehouse, data mesh, compliance, multi-cloud strategy	9 topics + 1 capstone

What you need¶

Python 3.10+ and pip. Level 1 uses only the standard library and pandas (plus pyarrow for Parquet) — no cloud account, no API key, everything runs locally.
Later levels introduce Airflow concepts, Delta Lake/Iceberg/Hudi table formats, and streaming engines; each lesson states exactly what to install (or simply describes, conceptually, since some tools need a cluster).

How to use this site¶

Work through each level in order — later modules assume earlier ones.
Every Level 1 topic page has runnable code — copy it into a local .py file and run it. Every output block on this level was carefully reasoned through against the actual pandas/pyarrow APIs, and is noted where it wasn't literally executed in a live environment.
Each level ends with a project or capstone that combines everything learned in that level.
Use the search bar (top of the page) to jump straight to a topic.

Start here → Level 1 · Entry

ETL and data lakes sit next to data engineering, analytics, and SQL. Sister sites cover the neighboring ground: