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Azure Data Engineering Cookbook

You're reading from   Azure Data Engineering Cookbook Design and implement batch and streaming analytics using Azure Cloud Services

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800206557
Length 454 pages
Edition 1st Edition
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Authors (2):
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Nagaraj Venkatesan Nagaraj Venkatesan
Author Profile Icon Nagaraj Venkatesan
Nagaraj Venkatesan
Ahmad Osama Ahmad Osama
Author Profile Icon Ahmad Osama
Ahmad Osama
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Toc

Table of Contents (11) Chapters Close

Preface 1. Chapter 1: Working with Azure Blob Storage 2. Chapter 2: Working with Relational Databases in Azure FREE CHAPTER 3. Chapter 3: Analyzing Data with Azure Synapse Analytics 4. Chapter 4: Control Flow Activities in Azure Data Factory 5. Chapter 5: Control Flow Transformation and the Copy Data Activity in Azure Data Factory 6. Chapter 6: Data Flows in Azure Data Factory 7. Chapter 7: Azure Data Factory Integration Runtime 8. Chapter 8: Deploying Azure Data Factory Pipelines 9. Chapter 9: Batch and Streaming Data Processing with Azure Databricks 10. Other Books You May Enjoy

Chapter 3: Analyzing Data with Azure Synapse Analytics

Azure Synapse Analytics, formerly known as SQL Data Warehouse, combines data warehousing and big data analytics to provide a unified experience to extract, load, and transform data. Azure Synapse has the following features—Synapse SQL – T-SQL-based analytics (SQL pools and SQL on-demand), Spark Analytics, Synapse pipelines, and Synapse studio. At the time of writing this book, all features except Synapse SQL are in preview.

Azure Synapse Analytics is important to learn as data warehousing is an important part of data engineering and big data solutions.

Azure Synapse SQL can be used to quickly load data from sources and perform transformations. The transformation queries are fast as Azure Synapse SQL uses massive parallel processing (MPP) architecture to process the queries.

In this chapter, we'll cover the following recipes:

  • Provisioning and connecting to an Azure Synapse SQL pool using PowerShell...
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