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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

Product type Paperback

Published in Apr 2022

Publisher Packt

ISBN-13 9781803231501

Length 238 pages

Edition 1st Edition

Languages

PySpark

Tools

Azure

Concepts

Data Science

Authors (2):

Gaurav Agarwal(BLR)

Meenakshi Muralidharan

View More author details

Table of Contents (11) Chapters

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 the resource consumption model of Synapse SQL

In this section, we will understand the architecture components of Synapse SQL, the definition of analytical storage, and how storage and compute work together to distribute computational processing of data across multiple nodes. Then we will cover the resource consumption model of Synapse SQL, which enables you to choose the right model for your analytical storage and queries.

Architecture components of Synapse SQL

Synapse SQL has two main types of SQL pools for analytical storage:

Dedicated SQL
Serverless SQL

Dedicated SQL

The Synapse dedicated SQL underlying architecture is node-based. Applications issue T-SQL to a control node, which is considered a single-entry point for Synapse SQL.

The Synapse SQL control node utilizes a massively parallel processing distributed query engine to optimize queries for parallel processing and then splits them into different sets of queries for each compute...

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Authors (2)

Agarwal(BLR)

Agarwal(BLR)

Gaurav Agarwal is a cloud solution architect at Microsoft India Corp. Ltd, working closely with Microsoft clients on Azure data and AI, Azure ML, big data, IoT, and Power BI. He has extensive experience in architecting and transforming data solutions for the modern cloud, and expertise in building large-scale enterprise data warehouse solutions.

See other products by Agarwal(BLR)

Muralidharan

Muralidharan

Meenakshi Muralidharan leads modern data platforms and is a renowned chief architect for an Indian multinational information technology services and consulting company. She has extensive experience in building large scale data platforms and applications in Microsoft Azure.

See other products by Muralidharan

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