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ETL with Azure Cookbook
ETL with Azure Cookbook

ETL with Azure Cookbook: Practical recipes for building modern ETL solutions to load and transform data from any source

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ETL with Azure Cookbook

Chapter 2: Introducing ETL

When I first started in the data warehousing business, something like 20 years ago, I was asked in an interview to define ETL. Being at my first job interview, I had no clue what the interviewer meant by ETL. Luckily, the interviewer kind of liked me and hired me anyway. He told me that I would know all about ETL quite soon. Being in data warehouse businesses for many years, and more recently a data engineer, ETL is what has kept me busy most of the time since then.

ETL stands for Extract, Transform, and Load. ETL is a data moving technique that has been used in various forms since the first enterprise data warehouses' inceptions.

Microsoft formalized the ETL concept near the end of the 1990s with a tool called DTS: Data Transformation Service. This ETL tool, aimed at helping database administrators load data into and from SQL Server, used SQL and ActiveX to move and transform data on-premises.

Microsoft brought its ETL tool to the cloud with...

Creating a SQL Azure database

To do ETL, we need to have a source to query data and a target (often called a sink) to land the transformed data. Our first recipe will create a simple SQL Azure database that will be used as both the source and sink in all recipes of this chapter.

Getting ready

In Chapter 1, Getting Started with Azure and SSIS 2019, you were shown how to install SQL Server on-premises, SQL Server Management Studio (SSMS), and Visual Studio 2019 with the SSIS extension. This chapter will show you how to set up SQL Server in Azure and the recipes will use this version of SQL Server.

How to do it…

In a browser, go to the Microsoft Azure portal using the following URL: https://portal.azure.com/#home.

On the main portal page, we'll create a resource group to group all Azure services together. It's much easier to find and manage them later.:

  1. From the main portal page, click on the + sign to create a new resource, as shown in the...

Connecting SQL Management Studio to Azure SQL

This recipe will show you how to connect to our Azure SQL database from your computer.

Getting ready

This recipe assumes that you have installed SSMS.

How to do it…

This is a very short and simple recipe! Let's begin by opening SSMS:

  1. We're going to connect to the Azure database that we created in the previous recipe, Creating a SQL Azure database. Log into your Azure subscription and navigate to the ETLInAzureCookBook resource group.
  2. Click on the AdventureWorksLT SQL database to open its blade. Copy the server name found at the top right as shown in the following screenshot:

    Figure 2.14 – Get the server name from the Azure subscription

  3. Using SSMS, log into the server using the username and password that we used in Step 7 of the previous recipe: Creating a SQL Azure database. Once connected, the Object Explorer shows the database and its related objects. Expanding the Tables folder lists...

Creating a simple ETL package

This recipe will show you how to build a simple ETL package that does the following:

  • Extract data from the AdventureWorksLT SalesLT.Customer table.
  • Transform data: Concatenating all customer-name-related information into a single column called FullName.
  • Load the data into a new table called SalesLT.CustomerFullName.

The table we create will be used in the final recipe in this chapter, Loading data before its transformation, to explain ELT.

Getting ready

This recipe assumes that you have installed SSMS, Visual Studio 2019, and SSIS.

How to do it…

Let's begin by opening Visual Studio 2019:

  1. Select Create a new project from the main screen.
  2. Select Integration Services Project from the screen that appears, as shown in the following screenshot:

    Figure 2.16 – Visual Studio – creating a new Integration Services Project

  3. As shown in the following screenshot, name the project AdventureWorksETL...

Loading data before its transformation

ELT is very similar to ETL, but with a crucial difference: the order of the transform and load steps are inverted. This is very useful with big data in the cloud or when we do not have an ETL tool on-premises. This recipe will be much simpler than the previous one, as we'll implement ELT using a database, so no tools are involved here except for calling the ELT task.

It also relies on the previous recipe, Creating a simple ETL package, since we're going to use the SalesLT.CustomerFullName table data to implement the ELT pattern.

There are essentially two parts to this recipe:

  1. Extract and load data into our data lake. Here, we don't have a real data lake; we're using AdventureWorksLT on Azure to mimic the data lake concept.
  2. Transform the data inside the database using a simple SQL script. We're going to add the FullName column to the SalesLT.Customer table and update it using this script.

Getting...

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Key benefits

  • Understand the key components of an ETL solution using Azure Integration Services
  • Discover the common and not-so-common challenges faced while creating modern and scalable ETL solutions
  • Program and extend your packages to develop efficient data integration and data transformation solutions

Description

ETL is one of the most common and tedious procedures for moving and processing data from one database to another. With the help of this book, you will be able to speed up the process by designing effective ETL solutions using the Azure services available for handling and transforming any data to suit your requirements. With this cookbook, you’ll become well versed in all the features of SQL Server Integration Services (SSIS) to perform data migration and ETL tasks that integrate with Azure. You’ll learn how to transform data in Azure and understand how legacy systems perform ETL on-premises using SSIS. Later chapters will get you up to speed with connecting and retrieving data from SQL Server 2019 Big Data Clusters, and even show you how to extend and customize the SSIS toolbox using custom-developed tasks and transforms. This ETL book also contains practical recipes for moving and transforming data with Azure services, such as Data Factory and Azure Databricks, and lets you explore various options for migrating SSIS packages to Azure. Toward the end, you’ll find out how to profile data in the cloud and automate service creation with Business Intelligence Markup Language (BIML). By the end of this book, you’ll have developed the skills you need to create and automate ETL solutions on-premises as well as in Azure.

Who is this book for?

This book is for data warehouse architects, ETL developers, or anyone who wants to build scalable ETL applications in Azure. Those looking to extend their existing on-premise ETL applications to use big data and a variety of Azure services or others interested in migrating existing on-premise solutions to the Azure cloud platform will also find the book useful. Familiarity with SQL Server services is necessary to get the most out of this book.

What you will learn

  • Explore ETL and how it is different from ELT
  • Move and transform various data sources with Azure ETL and ELT services
  • Use SSIS 2019 with Azure HDInsight clusters
  • Discover how to query SQL Server 2019 Big Data Clusters hosted in Azure
  • Migrate SSIS solutions to Azure and solve key challenges associated with it
  • Understand why data profiling is crucial and how to implement it in Azure Databricks
  • Get to grips with BIML and learn how it applies to SSIS and Azure Data Factory solutions
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Length: 446 pages
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Language : English
ISBN-13 : 9781800203310
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Table of Contents

11 Chapters
Chapter 1: Getting Started with Azure and SSIS 2019 Chevron down icon Chevron up icon
Chapter 2: Introducing ETL Chevron down icon Chevron up icon
Chapter 3: Creating and Using SQL Server 2019 Big Data Clusters Chevron down icon Chevron up icon
Chapter 4: Azure Data Integration Chevron down icon Chevron up icon
Chapter 5: Extending SSIS with Custom Tasks and Transformations Chevron down icon Chevron up icon
Chapter 6: Azure Data Factory Chevron down icon Chevron up icon
Chapter 7: Azure Databricks Chevron down icon Chevron up icon
Chapter 8: SSIS Migration Strategies Chevron down icon Chevron up icon
Chapter 9: Profiling data in Azure Chevron down icon Chevron up icon
Chapter 10: Manage SSIS and Azure Data Factory with Biml Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(2 Ratings)
5 star 50%
4 star 0%
3 star 50%
2 star 0%
1 star 0%
Plamen Dimitrov Nov 29, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Disclaimer: I received a free eBook from the publisher in exchange for an unbiased review.I think the book was a pleasant surprise. I have purchased other 'cookbooks' in the past that have offered far less.This book goes into great detail and the recipes are easy to follow. I don't think it is a book for beginners. Some of the topics are pretty advanced. So if you want to learn things from scratch, it is probably not the right one for you. However, If you are ETL professional who wants to pick up something new quickly , I think it is excellent!
Amazon Verified review Amazon
Adwait Ullal Nov 29, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Disclaimer: I received a free eBook from the publisher in exchange for an unbiased reviewThe "ETL with Azure Cookbook" is a book that is valuable to the seasoned SSIS programmer who wishes to progress his/her career to the cloud.It deals with the cloud and its services such as Blob Storage, Data Lake 2 (which unfortunately is not yet compatible with SSIS), Azure Data Factory, etc. from a SSIS perspective.The Cookbook is limited to only a few recipes so don't expect any encyclopedic information.Overall, it is a good book for a SSIS programmer to get oriented to Azure services and learn some of the techniques for transferring data to them.
Amazon Verified review Amazon
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