Welcome to the official companion repository for the Apress book:
"The Data Lakehouse Revolution: Harnessing the Power of Databricks for Generative AI and Machine Learning"
by Rajaniesh Kaushikk — Microsoft MVP, Databricks MVP, and Databricks Champion.
This repository provides the hands-on code, Databricks notebooks, and datasets that accompany the book, allowing you to practice the concepts and apply them directly in your own Databricks environment.
📖 Book Link: The Data Lakehouse Revolution on Amazon
In today’s world of AI-driven insights, organizations need a platform that combines the scalability of data lakes with the performance of data warehouses. This book introduces the data lakehouse paradigm, built on Databricks, and shows you how to leverage it for machine learning, generative AI, and retrieval-augmented generation (RAG).
You’ll move step by step through data preparation, model building, deployment, and governance with practical labs and industry examples.
The repository follows the chapter structure of the book. Each chapter folder contains notebooks (.dbc and .ipynb) and supporting files.
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Chapter 1: Getting Started with Databricks
Set up your Databricks environment, explore the workspace, and understand the fundamentals of the data lakehouse. -
Chapter 2: Introduction to Machine Learning and Data Lakehouses
Learn how ML workflows integrate with the data lakehouse paradigm. -
Chapter 3: Data Preparation and Management
Discover techniques for cleaning, transforming, and managing data for ML. -
Chapter 4: Building Machine Learning Models
Create and train models using Databricks ML and Spark MLlib. -
Chapter 5: AutoML and Model Optimization
Accelerate ML development using AutoML for algorithm selection and hyperparameter tuning. -
Chapter 6: Deploying Machine Learning Models
Use MLflow for model registration, deployment strategies, and lifecycle management. -
Chapter 7: Advanced Topics in Machine Learning
Dive into explainable AI, ethical considerations, and production best practices. -
Chapter 8: Lakehouse AI and Retrieval-Augmented Generation (RAG)
Build RAG workflows to combine LLMs with enterprise data securely. -
Chapter 9: Conclusion and Next Steps
Review lessons learned and plan the roadmap for your lakehouse journey.
To run the examples, you’ll need:
- A Databricks Workspace (Community or Enterprise edition)
- Databricks Runtime ML (latest recommended)
- Python 3.9+
- Required libraries (install via
requirements.txt):pip install -r requirements.txt