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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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Toc

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

Technical requirements

Before starting, make sure that your docker-compose images are up and running, and open the JupyterLab server running on the localhost (http: //127 .0.0. 1:8888/ lab). Also, ensure that you have cloned the Git repo for this book and have access to the notebook and data used in this chapter.

Remember to stop all services defined in the docker-compose file for this book when you are done running the code examples. You can do this by executing this command:

$ docker-compose stop

You can find the notebooks and data for this chapter at https://github.com/PacktPublishing/Data-Engineering-with-Databricks-Cookbook/tree/main/Chapter01.

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