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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

Learning about Databricks Pools

In this section, we will dive deeper into Azure Databricks Pools. We will start by creating a pool, attaching a cluster to a pool, and then learning about the best practices when using Pools in Azure Databricks.

Creating a pool

To create a pool, head over to the Databricks workspace. Then, click on Compute, select Pools, and click on + Create Pool. This will open a page where we need to define the pool's configuration, as shown in the following screenshot:

Figure 4.7 – Creating a pool in Azure Databricks

Let's discuss the configurations one by one:

  • Name: We need to give the pool a suitable name.
  • Min Idle: This defines the minimum number of idle instances that will be contained in the pool at any given time. These instances do not terminate and when consumed by a cluster, they will be replaced by another set of idle instances.
  • Max Capacity: This defines the maximum number of instances that...
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