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Optimizing Databricks Workloads

You're reading from  Optimizing Databricks Workloads

Product type Book
Published in Dec 2021
Publisher Packt
ISBN-13 9781801819077
Pages 230 pages
Edition 1st Edition
Languages
Authors (3):
Anirudh Kala Anirudh Kala
Profile icon Anirudh Kala
Anshul Bhatnagar Anshul Bhatnagar
Profile icon Anshul Bhatnagar
Sarthak Sarbahi Sarthak Sarbahi
Profile icon Sarthak Sarbahi
View More author details

Table of Contents (13) Chapters

Preface 1. Section 1: Introduction to Azure Databricks
2. Chapter 1: Discovering Databricks 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

Mounting Azure Data Lake in Databricks

Azure Data Lake (storage account) is a limitless data lake service managed by Microsoft. It allows the storage and analysis of big data of all forms: structured, semi-structured, and unstructured. It is built to accommodate big data analytics by integrating with Hadoop and Spark. We will be using Azure Data Lake quite often in this book. So, it is important for us to learn its integration with Azure Databricks.

Creating an Azure Data Lake instance

Now, we will create an Azure Data Lake instance and use it as our primary data store in the Azure environment:

  1. Go to Azure's website (portal.azure.com) and sign in to the portal. Open an existing resource group, then click on Create and then on Marketplace. Search for Storage account. The following window opens up:

    Figure 2.2 – Creating a storage account in Azure

  2. Click on Create. Set the Resource group, Storage account name, and Region. Set Performance to Standard: Recommended...
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