Mastering Data Analytics—Build End To End REAL Data Lakehouse with MinIO...

Mastering Data Analytics — Beginner’s Guide: Understand and Build a Lakehouse on Your Local Machine with PostgreSQL, MinIO (AWS S3 Object Storage) and Docker — Part5

Ready to level up your data engineering skills? In this comprehensive, step-by-step tutorial, we move beyond a basic setup and build a production-grade Data Lakehouse on your local machine. Learn how to fix the limitations of a file-system-based approach by integrating a true S3-compatible object storage layer with MinIO.

We'll guide you through the entire process: from setting up the infrastructure with Docker Compose to writing a Python ETL pipeline that extracts raw data, transforms it, and loads it into a PostgreSQL data warehouse. You'll also learn how to optimize your data by converting it to Parquet for high-speed analytics.

This architecture is scalable, cost-effective, and cloud-ready, giving you the perfect foundation for any modern data platform.

By the end of this video, you will be able to build :

1. A scalable Data Lake using MinIO (S3-compatible object storage).

2. An automated Python ETL pipeline to process and move data from MinIO To Data Warehouse in Postgres then Create Parquet Format & upload back to MinIO for AI/Ml and Data Science.

3. A powerful PostgreSQL Data Warehouse for analytics.

4. An optimized data layer with Parquet files.

5. A metadata catalog to track all your data objects.

https://www.youtube.com/watch?v=s9AnuDkdYxo