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Cloud Scale Analytics with Azure Data Services

You're reading from  Cloud Scale Analytics with Azure Data Services

Product type Book
Published in Jul 2021
Publisher Packt
ISBN-13 9781800562936
Pages 520 pages
Edition 1st Edition
Languages
Author (1):
Patrik Borosch Patrik Borosch
Profile icon Patrik Borosch

Table of Contents (20) Chapters

Preface Section 1: Data Warehousing and Considerations Regarding Cloud Computing
Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses Chapter 2: Connecting Requirements and Technology Section 2: The Storage Layer
Chapter 3: Understanding the Data Lake Storage Layer Chapter 4: Understanding Synapse SQL Pools and SQL Options Section 3: Cloud-Scale Data Integration and Data Transformation
Chapter 5: Integrating Data into Your Modern Data Warehouse Chapter 6: Using Synapse Spark Pools Chapter 7: Using Databricks Spark Clusters Chapter 8: Streaming Data into Your MDWH Chapter 9: Integrating Azure Cognitive Services and Machine Learning Chapter 10: Loading the Presentation Layer Section 4: Data Presentation, Dashboarding, and Distribution
Chapter 11: Developing and Maintaining the Presentation Layer Chapter 12: Distributing Data Chapter 13: Introducing Industry Data Models Chapter 14: Establishing Data Governance Other Books You May Enjoy

Backing up and DR in Azure Synapse

There are several aspects that you need to consider when you are planning backup and DR in your Azure Synapse workspace. You'll need to take care of three main areas:

  • Data
  • Developed artifacts
  • Infrastructure setup

Backing up data

You will first look after your data and take care that you don't lose your biggest assets here. The two areas that you want to consider could be the following:

  • Storage accounts/data lakes
  • Dedicated SQL pools

As the serverless SQL pools and Spark pools are talking to the data lake, mainly you will cover those by backing up the data lake.

Backing up storage accounts/data lakes

A data lake/storage account can be prepared for DR in different ways. There are the following:

  • Automated redundancy built into the service. You have already learned about this in Chapter 3, Understanding the Data Lake Storage Layer. You can decide on the zone or geo-redundant storage...
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