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Azure Synapse Analytics Cookbook

You're reading from   Azure Synapse Analytics Cookbook Implement a limitless analytical platform using effective recipes for Azure Synapse

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781803231501
Length 238 pages
Edition 1st Edition
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Authors (2):
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Gaurav Agarwal(BLR) Gaurav Agarwal(BLR)
Author Profile Icon Gaurav Agarwal(BLR)
Gaurav Agarwal(BLR)
Meenakshi Muralidharan Meenakshi Muralidharan
Author Profile Icon Meenakshi Muralidharan
Meenakshi Muralidharan
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Table of Contents (11) Chapters Close

Preface 1. Chapter 1: Choosing the Optimal Method for Loading Data to Synapse 2. Chapter 2: Creating Robust Data Pipelines and Data Transformation FREE CHAPTER 3. Chapter 3: Processing Data Optimally across Multiple Nodes 4. Chapter 4: Engineering Real-Time Analytics with Azure Synapse Link Using Cosmos DB 5. Chapter 5: Data Transformation and Processing with Synapse Notebooks 6. Chapter 6: Enriching Data Using the Azure ML AutoML Regression Model 7. Chapter 7: Visualizing and Reporting Petabytes of Data 8. Chapter 8: Data Cataloging and Governance 9. Chapter 9: MPP Platform Migration to Synapse 10. Other Books You May Enjoy

Using materialized views to improve performance

In this recipe, you will learn how materialized views can help in solving complex queries that are required for analytical purposes and how you can gain performance. You will learn how to create the materialized view, and when and why to use it in the SQL dedicated pool.

Materialized views are the best option in a large data warehouse, as the data is stored in a pre-processed format, unlike standard views. When you execute a query with a materialized view, it internally keeps processed data within the dedicated SQL pool, just like a physical SQL table.

Getting ready

Before you begin, make sure you have the following:

  • Make sure you have the dedicated SQL pool available in the Synapse workspace.
  • You need to ensure that the table in which you are creating a materialized view has a qualifying column that can participate in the columnstore index. You can refer to this document for a list of datatypes that can participate...
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