Mastering Data Engineering and Analytics with Databricks: A Hands-on Guide to Build Scalable Pipelines Using Databricks, Delta Lake, and MLflow (English Edition)

Kumar, Manoj; AVA, Orange

ISBN 10: 8196862040 / ISBN 13: 9788196862046

Published by Orange Education Pvt. Ltd, 2024

About this Item

Seller Inventory # 26403088735

Bibliographic Details

• Title: Mastering Data Engineering and Analytics with Databricks: A Hands-on Guide to Build Scalable Pipelines Using Databricks, Delta Lake, and MLflow (English Edition)
• Publisher: Orange Education Pvt. Ltd
• Publication Date: 2024
• Language: English
• ISBN 10: 8196862040
• ISBN 13: 9788196862046
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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

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.

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