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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 ML components in Databricks

The Databricks workspace is broadly divided into two personas: Data Science and Engineering and Machine Learning. We've already looked at the Data Science and Engineering persona. In this chapter, we will understand and work with elements in the Machine Learning persona. This workspace persona consists of additional tabs in the left pane. These include Experiments, Feature Store, and Models. To switch to the Machine Learning workspace, click on Data Science and Engineering in the left pane and select Machine Learning. This brings up the new persona, as illustrated in the following screenshot:

Figure 3.1 – Machine Learning workspace in Databricks

Let's now discuss the three most important elements of this workspace, as follows:

  • Experiments: This section gives access to all the MLflow experiments across the workspace. We will work with this section once we start learning about MLflow.
  • Feature...
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