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.

Table of Contents

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