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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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Toc

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

Working with serverless SQL pool

Azure Synapse Analytics has serverless SQL pool endpoints that are primarily used to query data in Azure Data Lake (Parquet, Delta Lake, delimited text formats), Azure Cosmos DB, and Dataverse.

We can access the data using T-SQL queries without the need to copy and load data in a SQL store through serverless SQL pool. Serverless SQL pool is ideally a wrapper service for interactive querying and distributed data processing for large-scale analysis of big data systems. It is a completely serverless and managed service offering from Microsoft Azure, built with fault tolerance, high reliability, and high performance for larger datasets.

Serverless SQL pool is suitable for the following scenarios:

  • Basic exploration and discovery where data in Azure Data Lake Storage (ADLS)with different formats such as Parquet, CSV, Delta, and JSON can be used to derive insights.
  • A relational abstraction layer on top of raw data without transformation,...
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