Books Written by Rajaniesh Kaushikk

Book 1. The Data Lakehouse Revolution: Harnessing the Power of Databricks for Generative AI and Machine Learning by Rajaniesh Kaushikk

This book provides a comprehensive, forward-looking playbook for modern data and AI practitioners working with the lakehouse paradigm and the Databricks platform. It arms engineers, data scientists, and leaders with both conceptual understanding and hands-on workflows to unlock the full potential of generative AI and machine learning at scale. Amazon

Summary
In an era where organizations are increasingly challenged to ingest, process, and analyze massive, varied datasets — while delivering timely, accurate, and intelligent insights — the lakehouse architecture (blending characteristics of data lakes and data warehouses) has emerged as a strategic enabler. Taking the Databricks platform as the unifying engine, this book guides you through the end-to-end lifecycle of data and AI: from data ingestion and preparation to model development and optimization, and finally to deployment, monitoring, governance, and generative AI workflows grounded in enterprise data. Apress

What you’ll learn

• How to get started and navigate the Databricks environment: the unified data lifecycle, collaborative notebooks, autoscaling compute, cloud-native operations. E-Bookshelf
• Foundational machine learning concepts and how they map to the lakehouse: supervised vs unsupervised vs reinforcement, feature engineering, and model evaluation. E-Bookshelf
• Preparing and managing data at scale: ingestion of batch/stream, cleaning, transformations, Delta Lake, schema enforcement, metadata, governance with Unity Catalog. E-Bookshelf
• Building and tuning machine learning models within Databricks: using MLflow, AutoML, hyperparameter tuning, model versioning, and deployment. Google Books
• Advanced topics: model deployment (batch, real-time, edge), monitoring for drift/performance, explainability (e.g., SHAP/LIME), and ethical and governance aspects of AI systems. E-Bookshelf
• Generative AI and Retrieval-Augmented Generation (RAG): how to build GenAI systems that leverage vector search, embeddings, LLMs, and enterprise data via the lakehouse architecture. Google Books

Why this book matters
With AI moving from experiments to production, the real differentiator is not just model accuracy — but the infrastructure, data architecture, governance, and operationalization of intelligence. This book fills the gap by offering a cohesive view of how to build intelligent systems in a modern, scalable manner. For professionals aiming to lead data/AI initiatives or modernize their stack, this is a timely and practical guide.

Book 2: Databricks Certified Generative AI Engineer Associate Study Guide by Rajaniesh Kaushikk

This book serves as both a certification study guide and a practical handbook for building generative AI solutions using the Databricks platform. It’s designed for professionals who want to sharpen their GenAI competencies, deploy real-world LLM-enabled apps, and align with the official certification path. O’Reilly Media

Summary
As generative AI and large-language-model (LLM)-driven applications become mainstream, organizations need engineers who can not only design intelligence solutions but also embed them responsibly into robust platforms. This guide walks you through the core topics of the official Databricks Certified Generative AI Engineer Associate exam (Design Applications, Data Preparation, Application Development, Deployment, Governance/Monitoring). Databricks

Through hands-on labs, case studies, exam-style questions, and real-world workflows, you’ll learn how to:

• Understand generative AI fundamentals: LLMs, vector databases, prompt engineering, chains and agents. O’Reilly Media
• Prepare and manage enterprise-scale data using Delta Lake and the lakehouse framework to feed GenAI systems. O’Reilly Media
• Build and deploy GenAI solutions on Databricks: RAG workflows, model serving, MLflow tracking, and efficient pipelines. O’Reilly Media
• Integrate governance, monitoring, security, and responsible AI practices (e.g. via Unity Catalog) to ensure GenAI systems remain reliable, auditable, and scalable. O’Reilly Media

Who this is for

• Data engineers and AI practitioners who are working with or planning to work with Databricks and GenAI.
• Professionals preparing for the Databricks GenAI Engineer Associate certification.
• Teams wanting to elevate their GenAI maturity from experiment to production, with generative workflows that scale and govern.

Why you’ll find it valuable
GenAI projects often stall not because of model architecture, but because of data readiness, infrastructure, governance, deployment, and monitoring. This study guide doesn’t just cover exam topics — it bridges the gap between learning and doing. It provides actionable insights and best practices for operationalizing generative AI with enterprise rigor.