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Azure Data Engineering Cookbook
Azure Data Engineering Cookbook

Azure Data Engineering Cookbook: Design and implement batch and streaming analytics using Azure Cloud Services

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Profile Icon Ahmad Osama Profile Icon Nagaraj Venkatesan
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₹799.99 ₹2919.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (12 Ratings)
eBook Apr 2021 454 pages 1st Edition
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Arrow left icon
Profile Icon Ahmad Osama Profile Icon Nagaraj Venkatesan
Arrow right icon
₹799.99 ₹2919.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.2 (12 Ratings)
eBook Apr 2021 454 pages 1st Edition
eBook
₹799.99 ₹2919.99
Paperback
₹3649.99
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Renews at ₹800p/m
eBook
₹799.99 ₹2919.99
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₹3649.99
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Renews at ₹800p/m

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Azure Data Engineering Cookbook

Chapter 2: Working with Relational Databases in Azure

Microsoft Azure provides Azure SQL Database, PostgreSQL, and MySQL as Database-as-a-Service offerings. We can create an instance of these databases without worrying about the installation, administration, infrastructure, and upgrades.

Needless to say that we can install any of the available Relational Database Management System (RDBMS) databases, such as Oracle or DB2, on an Azure virtual machine (VM).

In a data pipeline, we can use any of the RDBMS databases as either a source or a destination. In this chapter, we'll learn how to provision, connect to, manage, maintain, and secure these databases.

We'll cover the following recipes in this chapter:

  • Provisioning and connecting to an Azure SQL database using PowerShell
  • Provisioning and connecting to an Azure PostgreSQL database using the Azure CLI
  • Provisioning and connecting to an Azure MySQL database using the Azure CLI
  • Implementing active geo-replication for an Azure SQL database using PowerShell
  • Implementing an auto-failover group for an Azure SQL database using PowerShell
  • Implementing vertical scaling for an Azure SQL database using PowerShell
  • Implementing an Azure SQL database elastic pool using PowerShell
  • Monitoring an Azure SQL database using the Azure portal

Provisioning and connecting to an Azure SQL database using PowerShell

In this recipe, we'll learn how to create and connect to an Azure SQL Database instance. Azure SQL Database comes in three failovers: standalone Azure SQL Database, Azure SQL Database elastic pools, and managed instances. In this recipe, we'll create a standalone Azure SQL database.

Getting ready

In a new PowerShell window, execute the Connect-AzAccount command to log in to your Microsoft Azure account.

How to do it…

Let's begin by provisioning Azure SQL Database.

Provisioning Azure SQL Database

The steps are as follows:

  1. Execute the following PowerShell command to create a new resource group:
    New-AzResourceGroup -Name packtade -Location "central us"
  2. Execute the following query to create a new Azure SQL server:
    #create credential object for the Azure SQL Server admin credential
    $sqladminpassword = ConvertTo-SecureString 'Sql@Server@1234' -AsPlainText -Force
    $sqladmincredential = New-Object System.Management.Automation.PSCredential ('sqladmin', $sqladminpassword)
    # create the azure sql server
    New-AzSqlServer -ServerName azadesqlserver -SqlAdministratorCredentials $sqladmincredential -Location "central us" -ResourceGroupName packtade

    You should get a similar output as shown in the following screenshot:

    Figure 2.1 – Creating a new Azure SQL server

    Figure 2.1 – Creating a new Azure SQL server

  3. Execute the following query to create a new Azure SQL database:
    New-AzSqlDatabase -DatabaseName azadesqldb -Edition basic -ServerName azadesqlserver -ResourceGroupName packtade

    You should get an output as shown in the following screenshot:

Figure 2.2 – Creating a new Azure SQL database

Figure 2.2 – Creating a new Azure SQL database

Connecting to an Azure SQL database

To connect to an Azure SQL database, let's first whitelist the IP in the Azure SQL Server firewall:

  1. Execute the following command to whitelist the public IP of the machine to connect to an Azure SQL database. (This recipe assumes that you are connecting from your local system. To connect from a system other than your local system, change the IP in the following command.) Execute the following command in the PowerShell window to whitelist the machine's public IP in the Azure SQL Server firewall:
    $clientip = (Invoke-RestMethod -Uri https://ipinfo.io/json).ip
    New-AzSqlServerFirewallRule -FirewallRuleName "home" -StartIpAddress $clientip -EndIpAddress $clientip -ServerName azadesqlserver -ResourceGroupName packtade

    You will get an output similar to the one shown in the following screenshot:

    Figure 2.3 – Creating a new Azure SQL Server firewall rule

    Figure 2.3 – Creating a new Azure SQL Server firewall rule

  2. Execute the following command to connect to an Azure SQL database from SQLCMD (SQLCMD comes with the SQL Server installation, or you can download the SQLCMD utility from https://docs.microsoft.com/en-us/sql/tools/sqlcmd-utility?view=sql-server-ver15):
    sqlcmd -S "azadesqlserver.database.windows.net" -U sqladmin -P "Sql@Server@1234" -d azadesqldb

    Here's the output:

Figure 2.4 – Connecting to an Azure SQL database

Figure 2.4 – Connecting to an Azure SQL database

How it works…

We first execute the New-AzSQLServer command to provision a new Azure SQL server. The command accepts the server name, location, resource group, and login credentials. An Azure SQL server, unlike an on-premises SQL server, is not a physical machine or VM that is accessible to customers.

We then execute the New-AzSQLDatabase command to create an Azure SQL database. This command accepts the database name, the Azure SQL server name, the resource group, and the edition. There are multiple SQL database editions to choose from based on the application workload. However, for the sake of this demo, we will create a basic edition.

To connect to an Azure SQL database, we first need to whitelist the machine's IP in the Azure SQL Server firewall. Only whitelisted IPs are allowed to connect to the database. To whitelist the client's public IP, we use the New-AzSQLServerfirewallrule command. This command accepts the server name, resource group, and start and end IPs. We can either whitelist a single IP or a range of IPs.

We can connect to an Azure SQL database from SQL Server Management Studio, SQLCMD, or a programming language using the appropriate SQL Server drivers. When connecting to an Azure SQL database, we need to specify the server name as azuresqlservername.database.windows.net, and then specify the Azure SQL database to connect to.

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Key benefits

  • Build highly efficient ETL pipelines using the Microsoft Azure Data services
  • Create and execute real-time processing solutions using Azure Databricks, Azure Stream Analytics, and Azure Data Explorer
  • Design and execute batch processing solutions using Azure Data Factory

Description

Data engineering is one of the faster growing job areas as Data Engineers are the ones who ensure that the data is extracted, provisioned and the data is of the highest quality for data analysis. This book uses various Azure services to implement and maintain infrastructure to extract data from multiple sources, and then transform and load it for data analysis. It takes you through different techniques for performing big data engineering using Microsoft Azure Data services. It begins by showing you how Azure Blob storage can be used for storing large amounts of unstructured data and how to use it for orchestrating a data workflow. You'll then work with different Cosmos DB APIs and Azure SQL Database. Moving on, you'll discover how to provision an Azure Synapse database and find out how to ingest and analyze data in Azure Synapse. As you advance, you'll cover the design and implementation of batch processing solutions using Azure Data Factory, and understand how to manage, maintain, and secure Azure Data Factory pipelines. You’ll also design and implement batch processing solutions using Azure Databricks and then manage and secure Azure Databricks clusters and jobs. In the concluding chapters, you'll learn how to process streaming data using Azure Stream Analytics and Data Explorer. By the end of this Azure book, you'll have gained the knowledge you need to be able to orchestrate batch and real-time ETL workflows in Microsoft Azure.

Who is this book for?

This book is for Data Engineers, Database administrators, Database developers, and extract, load, transform (ETL) developers looking to build expertise in Azure Data engineering using a recipe-based approach. Technical architects and database architects with experience in designing data or ETL applications either on-premise or on any other cloud vendor who wants to learn Azure Data engineering concepts will also find this book useful. Prior knowledge of Azure fundamentals and data engineering concepts is needed.

What you will learn

  • Use Azure Blob storage for storing large amounts of unstructured data
  • Perform CRUD operations on the Cosmos Table API
  • Implement elastic pools and business continuity with Azure SQL Database
  • Ingest and analyze data using Azure Synapse Analytics
  • Develop Data Factory data flows to extract data from multiple sources
  • Manage, maintain, and secure Azure Data Factory pipelines
  • Process streaming data using Azure Stream Analytics and Data Explorer

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Length: 454 pages
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Table of Contents

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

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.2
(12 Ratings)
5 star 41.7%
4 star 41.7%
3 star 8.3%
2 star 8.3%
1 star 0%
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Carles G. May 20, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Data Engineering is not something I was really interested in but to become an Azure architect someday, it is a must. Relational databases, storage accounts, Azure Synapse Analytics, Data Factory and some other applications are very well explained in this book. I like the structure of the book where from the beginning you start building some examples using the different ways that can be used in the following chapters and help to understand the concepts easily.Some examples and some parts of the book can be obviously found in the MS Documentation but I would have loved to have the Chapter 7 when I had to migrate SQL Integration Services to Azure. I would have saved many hours of reading! I feel like it has what I needed that day.You will need some Azure experience to understand better some concepts but to start with data engineering on Azure it is a good book.
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Amazon Customer Jun 27, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The format of this book is its strongest point. Its approach of breaking each knowledge area down into getting ready, how do do it and how it works is the key selling point and value in this book and sets it out apart from others that attempt to cover the same ground. Doing and understanding what you are doing are two different things, and why you are doing something is the real value than just doing .. a book that just has the step by step what to click with the explanation or understanding or vice versa a book that is purely theory and concepts can never hope to achieve the same impact. For this reason this book has real impact in the ability for someone to have real world job skills that would be valuable after using this book and will have continued value as its not just a book that you read once, understand the theory and then put down again; as it says its practical recipes and each time you come across a scenario that needs a solution, the recipe list in this book as it were is a great place to look of how to do something.
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SWABLE Apr 22, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book will be extremely useful for anyone who wants to implement Data Engineering in a very robust and step by step manner. The author has explained all the concepts very deeply and the content is pretty much relevant and up-to date.
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roog Jun 24, 2021
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The book was amazing and very rich with helpful guidelines
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Amanpreet K. Oct 13, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Really good descriptive book ….
Amazon Verified review Amazon
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