Curriculum
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1
Book Preview
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2
Introduction
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(Included in full purchase)
Introduction
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(Included in full purchase)
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3
Chapter 1 : Introducing Data Engineering with Databricks
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(Included in full purchase)
Introducing Data Engineering with Databricks
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(Included in full purchase)
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4
Chapter 2 : Setting Up a Databricks Environment for Data Engineering
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(Included in full purchase)
Setting Up a Databricks Environment for Data Engineering
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(Included in full purchase)
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5
Chapter 3 : Working with Databricks Utilities and Clusters
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(Included in full purchase)
Working with Databricks Utilities and Clusters
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(Included in full purchase)
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6
Chapter 4 : Extracting and Loading Data Using Databricks
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(Included in full purchase)
Extracting and Loading Data Using Databricks
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(Included in full purchase)
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7
Chapter 5 : Transforming Data with Databricks
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(Included in full purchase)
Transforming Data with Databricks
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(Included in full purchase)
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8
Chapter 6 : Handling Streaming Data with Databricks
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(Included in full purchase)
Handling Streaming Data with Databricks
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(Included in full purchase)
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9
Chapter 7 : Creating Delta Live Tables
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(Included in full purchase)
Creating Delta Live Tables
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(Included in full purchase)
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10
Chapter 8 : Data Partitioning and Shuffling
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(Included in full purchase)
Data Partitioning and Shuffling
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(Included in full purchase)
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11
Chapter 9 : Performance Tuning and Best Practices
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(Included in full purchase)
Performance Tuning and Best Practices
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(Included in full purchase)
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12
Chapter 10 : Workflow Management
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(Included in full purchase)
Workflow Management
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(Included in full purchase)
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13
Chapter 11 : Databricks SQL Warehouse
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(Included in full purchase)
Databricks SQL Warehouse
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(Included in full purchase)
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14
Chapter 12 : Data Storage and Unity Catalog
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(Included in full purchase)
Data Storage and Unity Catalog
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(Included in full purchase)
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15
Chapter 13 : Monitoring Databricks Clusters and Jobs
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(Included in full purchase)
Monitoring Databricks Clusters and Jobs
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(Included in full purchase)
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16
Chapter 14 : Production Deployment Strategies
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(Included in full purchase)
Production Deployment Strategies
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(Included in full purchase)
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Chapter 15 : Maintaining Data Pipelines in Production
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(Included in full purchase)
Maintaining Data Pipelines in Production
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(Included in full purchase)
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18
Chapter 16 : Managing Data Security and Governance
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(Included in full purchase)
Managing Data Security and Governance
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(Included in full purchase)
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19
Chapter 17 : Real-World Data Engineering Use Cases with Databricks
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(Included in full purchase)
Real-World Data Engineering Use Cases with Databricks
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(Included in full purchase)
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20
Chapter 18 : AI and ML Essentials
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(Included in full purchase)
AI and ML Essentials
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(Included in full purchase)
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21
Chapter 19 : Integrating Databricks with External Tools
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(Included in full purchase)
Integrating Databricks with External Tools
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(Included in full purchase)
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22
INDEX
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(Included in full purchase)
INDEX
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(Included in full purchase)
About the course
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.
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About the Author
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.