Mastering Data Engineering and Analytics with Databricks

Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges

Key Features
● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow.
● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action.
● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines.
● Offers proven strategies to optimize workflows and avoid common pitfalls.

Book Description
In today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide.

Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics.

This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals.

Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space.

What you will learn
● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases.
● Optimize query performance and efficiently manage cloud resources for cost-effective data processing.
● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation.
● Build and deploy real-time data processing solutions for timely and actionable insights.
● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale.

Who is this book for?
This book is designed for data engineering students, aspiring data engineers, experienced data professionals, cloud data architects, data scientists and analysts looking to expand their skill sets, as well as IT managers seeking to master data engineering and analytics with Databricks. A basic understanding of data engineering concepts, familiarity with data analytics, and some experience with cloud computing or programming languages such as Python or SQL will help readers fully benefit from the book’s content.

SECTION 1
1. Introducing Data Engineering with Databricks
2. Setting Up a Databricks Environment for Data Engineering
3. Working with Databricks Utilities and Clusters
SECTION 2
4. Extracting and Loading Data Using Databricks
5. Transforming Data with Databricks
6. Handling Streaming Data with Databricks
7. Creating Delta Live Tables
8. Data Partitioning and Shuffling
9. Performance Tuning and Best Practices
10. Workflow Management
11. Databricks SQL Warehouse
12. Data Storage and Unity Catalog
13. Monitoring Databricks Clusters and Jobs
14. Production Deployment Strategies
15. Maintaining Data Pipelines in Production
16. Managing Data Security and Governance
17. Real-World Data Engineering Use Cases with Databricks
18. AI and ML Essentials
19. Integrating Databricks with External Tools
Index

Manoj Kumar is a seasoned professional with a unique blend of technical expertise, business acumen, and academic pursuits. His journey in the world of data and technology is a testament to his passion for continuous learning and innovation.

Manoj holds a B.Tech in Electronics and Communication and a PGDM in Operations Management, combining technical and managerial expertise to solve complex business problems through technology. With a passion for data, he transitioned into IT, specializing in cloud-based data solutions over the past 14 years, particularly in the FMCG and CPG sectors. His work focuses on architecting scalable solutions that drive big data analytics and digital transformation. Currently pursuing a doctorate in Generative AI at Golden Gate University, Manoj remains committed to advancing technology and leadership in AI-driven business solutions.

Manoj's 14-year journey in the industry is marked by significant achievements and contributions, bridging technical complexities and business needs. His work includes implementing cutting-edge data solutions, optimizing supply chains, and driving data-driven decision-making processes in the FMCG and CPG industries.

As an author, Manoj brings wealth of experience, academic rigor, and industry insights to his writing. His book on Mastering Data Engineering and Analytics with Databricks reflects his years of hands-on experience, academic knowledge, and vision for the future of data engineering in the cloud era.

When not working on technical projects, Manoj enjoys cooking and exploring new skills.

ABOUT TECHNICAL REVIEWERS

Nitil Dwivedi is a seasoned data engineer with a decade of experience in crafting innovative data solutions. He excels in leveraging cloud technologies, data virtualization, and visualization to address complex business challenges. Committed to continuous learning, Nitil expands his skills through new tech stacks and certifications.

With a proven track record of success across diverse industries – ranging from technology and retail to healthcare – Nitil has led research and development, technical consulting, and support teams, contributing significantly to  organizations such as RedHat, Logitech, and various IT and Services firms.

Currently, as a BI Architect at Tiger Analytics, Nitil empowers global clients to achieve business value through data-driven insights. He leverages reliable data engineering approaches and embraces cutting-edge technologies to drive impactful transformations. Nitil is passionate about fostering teams of skilled experts to achieve organizational excellence.

Satyendra Chauhan is a certified master data architect with 20 years of experience in architecting and delivering large, complex data and analytics solutions, including data integration, migration, data modernization, master data management, and advanced analytics solutions across financial services. In his current role, he is responsible for providing solutions and delivering data on cloud projects, which include migration, cloud-native development, predictive AI/ML model management, and Guidewire data integration.

A US patent holder, speaker, and faculty member at various data architecture and data mesh/data products classes, Satyendra mentors data leaders across the globe. An avid reader and runner, he has also participated in numerous half-marathons.