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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
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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

Chapter 4: Managing Spark Clusters

A Spark cluster in Azure Databricks is probably the most important entity in the service. Although it is managed for us from the infrastructure end, we must understand the right cluster setting for an environment.

In this chapter, we will learn about the best practices to manage our Spark clusters to optimize our workloads. We will also learn about the Databricks managed resource group, which will help us understand how Azure Databricks is provisioned.

We will learn how to optimize costs associated with Spark clusters with pools and spot instances. In the end, we will learn about the essential components of the Spark UI that can help us debug and optimize queries.

In this chapter, we will cover the following topics:

  • Designing Spark clusters
  • Learning about Databricks managed resource groups
  • Learning about Databricks Pools
  • Using spot instances
  • Following the Spark UI
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