Preface

Azure Synapse Analytics is an analytics platform offered by the Microsoft Azure cloud platform. This book will help you understand the basic concepts of Azure Synapse and get you familiar with how it works in practice, step by step. This book has been written in simple language and with plenty of diagrams to make it easier for you to understand the concepts.

Each main topic has a whole chapter dedicated to it, such that even the minor concepts are explained in detail. You just need to have a basic knowledge of SQL Data Warehouse and Azure generally to follow the topics in this book.

To fully understand Azure Synapse, you need to understand a few other technologies as well, such as Power BI, Azure Data Factory, and Azure Machine Learning. I have tried to cover these services and how they are integrated together with Azure Synapse. Overall, this book should leave anyone well equipped to start working on Azure's analytics platform within a week.

Who this book is for

This book is a must-buy for anyone who works with Azure's data services. However, anyone working with or studying big data will also find it helpful. AWS or Google data architects will also find this book very helpful in terms of comparing Synapse with their own big data analytics platforms. You need to have a basic knowledge of dedicated SQL pool and be familiar with Azure to understand all the concepts in this book. Some of the chapters are specific to data orchestration, Azure Machine Learning, and Power BI, so if you have prior knowledge of these topics, it will be easier for you to learn all the concepts covered in this book.

What this book covers

Chapter 1, Introduction to Azure Synapse, provides an overview of all the components that make up the Synapse workspace: dedicated SQL pool, Spark pools, Synapse pipelines, Azure Machine Learning, and Power BI. In this chapter, you will learn the basics of Synapse and how to create your first Synapse workspace.

Chapter 2, Considerations for Your Compute Environment, focuses on the compute environments of Synapse. This chapter will focus mainly on dedicated SQL pool, serverless SQL pools, and Spark pools. It will help you choose the correct environment for your business problem.

Chapter 3, Bringing Your Data to Azure Synapse, covers multiple options to bring your data from various sources to Azure Synapse. You will learn how to use different services to set up a connection with Azure Synapse.

Chapter 4, Using Synapse Pipelines to Orchestrate Your Data, focuses on Synapse pipelines, which are very similar to Azure Data Factory pipelines; however, you don't need to create a separate Data Factory pipeline for orchestration. Instead, you can perform all the operations you need to do directly within Synapse Studio.

Chapter 5, Using Synapse Link with Azure Cosmos DB, is where you will learn how you can perform analytics operations directly on Cosmos DB data without moving data. This chapter will help you understand how Synapse Link has reduced the total time required for running an analytics operation on Cosmos DB data by removing the need for data movement from Cosmos DB to Azure Synapse.

Chapter 6, Working with T-SQL in Azure Synapse, teaches you how to query data using T-SQL on Azure Synapse. This chapter will cover the pre-requisites and provide the details for sample data that can be used to perform some simple operations on Azure Synapse using T-SQL.

Chapter 7, Working with R, Python, Scala, .NET, and Spark SQL in Azure Synapse, covers how to query data using various coding languages on Azure Synapse. This chapter will cover the pre-requisites and provide details on sample data that can be used to perform simple operations on Azure Synapse using R, Python, Scala, .NET, and Spark SQL.

Chapter 8, Integrating a Power BI Workspace with Azure Synapse, explores how to integrate a Power BI workspace with Azure Synapse and how you can connect Azure Synapse data to Power BI Desktop.

Chapter 9, Perform Real-Time Analytics on Streaming Data, looks at how to perform real-time analytics on streaming data. This chapter focuses on bringing streaming data to Synapse and performing operations on this data using various languages.

Chapter 10, Generate Powerful Insights on Azure Synapse Using Azure Machine Learning, shows you how to integrate Azure Machine Learning with Azure Synapse. You will also learn how to use different languages to pair Azure Machine Learning with Azure Synapse.

Chapter 11, Performing Backup and Restore in Azure Synapse Analytics, is where you will learn how to use backup and restore in Azure Synapse SQL pools. You will learn about automatic and user-defined restore points. This chapter covers how a user can perform cross-subscription restores and geo-redundant restores as well.

Chapter 12, Securing Data on Azure Synapse, talks about how to secure customer data on Azure Synapse. It is very important to understand how you can keep your data safe. This chapter guides you on how you can enable all the best security measures in your Synapse workspace.

Chapter 13, Managing and Monitoring Synapse Workloads, focuses on manageability and monitoring resource utilization and query activity in Azure Synapse Analytics.

Chapter 14, Coding Best Practices, helps you to understand the best practices for performance and management. In this chapter, you will also learn about the best practices for dedicated SQL pools, serverless SQL pools, and Spark pools.

To get the most out of this book

Now let's look at the technical requirements for this book:

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Having the following pre-requisites will mean you can follow the book and understand the concepts covered:

• You must have a basic knowledge of the Azure portal.
• It would be helpful if you had prior knowledge of SQL Data Warehouse, Azure Data Factory, Power BI, and Azure Machine Learning.
• You should have an Azure subscription or access to any other subscription with contributor-level access.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/packtPublishing/Limitless-Analytics-with-Azure-Synapse/. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here:

https://static.packt-cdn.com/downloads/9781800205659_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We will use the following T-SQL code to create a UserData table in Synapse SQL."

A block of code is set as follows:

CREATE TABLE UserData (

  UserID INT,

  Name VARCHAR(200),

  EmailID  VARCHAR(200),

  State  VARCHAR(50),

  City VARCHAR(50)

)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

[default]

exten => s,1,Dial(Zap/1|30)

exten => s,2,Voicemail(u100)

exten => s,102,Voicemail(b100)

exten => i,1,Voicemail(s0)

Any command-line input or output is written as follows:

$ SubscriptionName="<YourSubscriptionName>"

$ ResourceGroupName="<YourResourceGroupName>"

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "For the Use existing data property under Data source, select Backup."

Tips or important notes

Appear like this.

Get in touch

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