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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

Part 2 – Data Engineering Capabilities within Databricks

In this part, we will comprehensively explore advanced Databricks features, guiding you through orchestrating and scheduling data pipelines, building dynamic pipelines with Delta Live Tables, enforcing data governance with Unity Catalog, and integrating DataOps and DevOps practices. It offers practical, hands-on guidance for enhancing data engineering processes, ensuring data quality and compliance, and promoting a culture of continuous improvement within the Databricks ecosystem.

This part contains the following chapters:

  • Chapter 8, Orchestration and Scheduling Data Pipeline with Databricks Workflows
  • Chapter 9, Building Data Pipelines with Delta Live Tables
  • Chapter 10, Data Governance with Unity Catalog
  • Chapter 11, Implementing DataOps and DevOps on Databricks
...
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