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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

Product type Paperback

Published in Dec 2021

Publisher Packt

ISBN-13 9781801819077

Length 230 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Big Data

Authors (3):

Anshul Bhatnagar

Sarthak Sarbahi

Anirudh Kala

View More author details

Table of Contents (13) Chapters

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

Using spot instances

In this section, we will go through a quick tutorial on leveraging spot instances while creating our Databricks cluster. Let's begin by making a new cluster in the Databricks workspace. Create the following configuration for the cluster:

Cluster Name: DB_cluster_with_spot
Cluster Mode: Standard
Databricks Runtime Version: Runtime: 8.3 (Scala 2.12, Spark 3.1.1)
Autoscaling: Disabled
Automatic Termination: After 30 minutes
Worker Type: Standard_DS3_v2
Driver Type: Standard_DS3_v2
Number of workers: 1
Spot instances: Enabled

It will take a few minutes for the cluster to start. The following screenshot shows that our cluster with spot instances has been initialized:

Figure 4.12 – Databricks cluster with spot instances

To confirm that we are using a cluster with spot instances, we can click on the JSON option to view the JSON structure of the cluster. We can find this option on the same...

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Authors (3)

Anirudh Kala

Anirudh Kala

Anirudh Kala is an expert in machine learning techniques, artificial intelligence, and natural language processing. He has helped multiple organizations to run their large-scale data warehouses with quantitative research, natural language generation, data science exploration, and big data implementation. He has worked in every aspect of data analytics using the Azure data platform. Currently, he works as the director of Celebal Technologies, a data science boutique firm dedicated to large-scale analytics. Anirudh holds a computer engineering degree from the University of Rajasthan and his work history features the likes of IBM and ZS Associates.

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Sarbahi

Sarbahi

Sarthak Sarbahi is a certified data engineer and analyst with a wide technical breadth and a deep understanding of Databricks. His background has led him to a variety of cloud data services with an eye toward data warehousing, big data analytics, robust data engineering, data science, and business intelligence. Sarthak graduated with a degree in mechanical engineering.

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Bhatnagar

Bhatnagar

Kartik Bhatnagar loves nature and likes to visit picturesque places. He is a technical architect in the big data analytics unit of Infosys. He is passionate about new technologies. He is leading the development work of Apache Storm and MarkLogic NoSQL for a leading bank. Kartik has a total 10 years of experience in software development for Fortune 500 companies in many countries. His expertise also includes the full Amazon Web Services (AWS) stack and modern open source libraries. He is active on the StackOverflow platform and is always eager to help young developers with new technologies. Kartik has also worked as a reviewer of a book called Elasticsearch Blueprints, Packt Publishing. In the future, he wants to work on predictive analytics.

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