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Optimizing Databricks Workloads

You're reading from   Optimizing Databricks Workloads Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801819077
Length 230 pages
Edition 1st Edition
Languages
Concepts
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Authors (3):
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Anshul Bhatnagar Anshul Bhatnagar
Author Profile Icon Anshul Bhatnagar
Anshul Bhatnagar
Sarthak Sarbahi Sarthak Sarbahi
Author Profile Icon Sarthak Sarbahi
Sarthak Sarbahi
Anirudh Kala Anirudh Kala
Author Profile Icon Anirudh Kala
Anirudh Kala
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Toc

Table of Contents (13) Chapters Close

Preface 1. Section 1: Introduction to Azure Databricks
2. Chapter 1: Discovering Databricks FREE CHAPTER 3. Chapter 2: Batch and Real-Time Processing in Databricks 4. Chapter 3: Learning about Machine Learning and Graph Processing in Databricks 5. Section 2: Optimization Techniques
6. Chapter 4: Managing Spark Clusters 7. Chapter 5: Big Data Analytics 8. Chapter 6: Databricks Delta Lake 9. Chapter 7: Spark Core 10. Section 3: Real-World Scenarios
11. Chapter 8: Case Studies 12. Other Books You May Enjoy

Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Azure Databricks Cookbook

Phani Raj, Vinod Jaiswal

ISBN: 978-1-78980-971-8

  • Read and write data from and to various Azure resources and file formats
  • Build a modern data warehouse with Delta Tables and Azure Synapse Analytics
  • Explore jobs, stages, and tasks and see how Spark lazy evaluation works
  • Handle concurrent transactions and learn performance optimization in Delta tables
  • Learn Databricks SQL and create real-time dashboards in Databricks SQL
  • Integrate Azure DevOps for version control, deploying, and productionizing solutions with CI/CD pipelines
  • Discover how to use RBAC and ACLs to restrict data access
  • Build end-to-end data processing pipeline for near real-time data analytics

Distributed Data Systems with Azure Databricks

Alan Bernardo Palacio

ISBN: 978-1-83864-721-6

  • Create ETLs for big data in...
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