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Data Engineering with Databricks Cookbook

You're reading from   Data Engineering with Databricks Cookbook Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781837633357
Length 438 pages
Edition 1st Edition
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Author (1):
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Pulkit Chadha Pulkit Chadha
Author Profile Icon Pulkit Chadha
Pulkit Chadha
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Table of Contents (16) Chapters Close

Preface 1. Part 1 – Working with Apache Spark and Delta Lake FREE CHAPTER
2. Chapter 1: Data Ingestion and Data Extraction with Apache Spark 3. Chapter 2: Data Transformation and Data Manipulation with Apache Spark 4. Chapter 3: Data Management with Delta Lake 5. Chapter 4: Ingesting Streaming Data 6. Chapter 5: Processing Streaming Data 7. Chapter 6: Performance Tuning with Apache Spark 8. Chapter 7: Performance Tuning in Delta Lake 9. Part 2 – Data Engineering Capabilities within Databricks
10. Chapter 8: Orchestration and Scheduling Data Pipeline with Databricks Workflows 11. Chapter 9: Building Data Pipelines with Delta Live Tables 12. Chapter 10: Data Governance with Unity Catalog 13. Chapter 11: Implementing DataOps and DevOps on Databricks 14. Index 15. Other Books You May Enjoy

The evolving landscape of data engineering

In recent years, the field of data engineering has undergone a transformative shift, with the demand for efficient, scalable, and collaborative solutions reaching unprecedented heights. This book addresses this paradigm shift by delving into the intricacies of Apache Spark, a versatile engine for big data processing, Databricks, a collaborative and cloud-based platform, and Delta Lake, an open source storage layer that enhances the reliability and consistency of your data workflows.

A pragmatic approach to data engineering

This cookbook goes beyond being a compilation of recipes; it’s a pragmatic guide aimed at empowering you to overcome real-world data engineering challenges. The recipes provided are designed to be practical, with step-by-step instructions, code snippets, and detailed explanations to facilitate a hands-on learning experience. Whether you are a seasoned data engineer or just embarking on your data journey, the book offers valuable insights and practical solutions to integrate these cutting-edge technologies seamlessly into your projects.

Key features

Some of the key objectives of this book are as follows:

  • In-depth recipes for the entire data engineering life cycle: Navigate through a comprehensive set of recipes covering data extraction, transformation, loading, and effective management within a Lakehouse architecture
  • Practical learning: Embrace a hands-on approach with detailed instructions, code examples, and explanations to ensure you gain practical expertise in applying these technologies to real-world scenarios
  • Best practices and optimization: Benefit from industry best practices and expert tips to optimize your data engineering workflows, building scalable, efficient, and easy-to-maintain solutions
  • Real-world challenges and solutions: Explore recipes addressing common challenges faced by data engineers in actual projects, providing practical insights for implementation
  • Collaboration and seamless integration: Leverage the collaborative capabilities of Databricks and learn how to seamlessly integrate these technologies into your existing data infrastructure, fostering a more efficient and collaborative environment

Embark on a journey to master the art of data engineering with Apache Spark, Databricks, and Delta Lake. This cookbook is not just a guide; it’s your companion in navigating the complexities of modern data engineering. Happy cooking!

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