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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

Processing and querying very large datasets

Synapse SQL uses distributed query processing, the data movement service, and scale-out architecture, leveraging the advantages of the scalability and flexibility of compute and storage. Data transformation is not required prior to loading it to Synapse SQL. We need to use the built-in massively parallel processing capabilities of Synapse, load data in parallel, and then perform the transformation.

Loading data using PolyBase external tables and COPY SQL statements is considered one of the fastest, most reliable, and scalable ways of loading data. We can use external data stored in ADLS and Azure Blob storage, and load data using the COPY statement. This data is then loaded to production tables and exposed as views, which creates a query view for the client applications to derive meaningful business insights.

Getting ready

We will be performing a series of steps in order to extract, load, and create a materialized view of data for...

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