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Hands-On Data Warehousing with Azure Data Factory
Hands-On Data Warehousing with Azure Data Factory

Hands-On Data Warehousing with Azure Data Factory: ETL techniques to load and transform data from various sources, both on-premises and on cloud

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Profile Icon Cote Profile Icon Michelle Gutzait Profile Icon Giuseppe Ciaburro
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€29.99
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.8 (10 Ratings)
eBook May 2018 284 pages 1st Edition
eBook
€29.99
Paperback
€36.99
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Renews at €18.99p/m
Arrow left icon
Profile Icon Cote Profile Icon Michelle Gutzait Profile Icon Giuseppe Ciaburro
Arrow right icon
€29.99
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.8 (10 Ratings)
eBook May 2018 284 pages 1st Edition
eBook
€29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€29.99
Paperback
€36.99
Subscription
Free Trial
Renews at €18.99p/m

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Hands-On Data Warehousing with Azure Data Factory

Getting Started with Our First Data Factory

OK, enough talk, let's get our hands dirty. In this chapter, we will cover the basics of the data factory and show you what the components discussed in the previous chapter are doing.

Here is what's needed for this chapter, as well as all others in this book:

  • An active Azure subscription: if you don't have one, you can use a trial version at https://azure.microsoft.com/en-in/free/.
  • Once we have an Azure subscription, we will create the following Azure components:
    • A resource group that will group everything we're going to create together
    • An ADF with a copy activity
    • An Azure Blob storage
    • A SQL Azure database

Resource group

To create a resource group, we need to log on to the Azure portal by typing the following URL in a browser: http://Portal.Azure.com.

From the resource panel, click on the + New icon and enter resource group in the search box, as shown in the following screenshot:

Clicking on the search results opens the Resource group blade. We simply need to click Create at the bottom of the screen.

As shown in the following screenshot, we'll fill the textboxes with the following properties:

  • Resource group name: ADFV2Book.
  • Subscription: Should be filled with the active subscription used to create the resource group.
  • Resource group location: We can choose any location we want. Location is not that important for a resource group but for compliance reasons, we should use a location where all our Azure artifacts will be created.

Once we click on Create at the bottom of the...

Azure Data Factory

Now, we'll create the factory. The goal of the exercise is to copy data from a SQL Server table and bring it in an Azure Blob storage.

Log in to the Azure portal (http://Portal.Azure.com). In the resource section, click the + New icon. Click on Data + Analytics and select Data Factory, as shown in the following screenshot:

The New data factory blade opens. As shown in the following screenshot, fill the textboxes with the following values:

  • Name: The name of the factory might be later registered as DNS. It should be unique if at all possible. To make it unique, we might use our initials in front of it. There are naming rules for data factories, which can be found at https://docs.microsoft.com/en-us/azure/data-factory/naming-rules.
  • Subscription: Should be filled with the active subscription used to create the data factory.
  • Resource Group: We're...

Azure Blob storage

At the heart of the Azure ecosystem is the blob storage service. This service, as its name indicates, provides file storage. There are different types of blobs, as not all of them are created equally. The next few sections will describe the different blobs and their usage.

Blob containers

Blob containers are the first level of blob storage. All blobs need a container as root access. It's like a folder in a standard operating system.

Types of blobs

Block blobs

Block blobs are used by most Azure data transfers. They can store application files (CSV, ZIP, and so on), tables used by NoSQL applications, and queues used by streaming services such as Azure ML (short for Azure Machine Learning). Throughout the examples in this book, we'll use block blobs as storage for some of our data transfers.

Page blobs

...

SQL Azure database

We'll now set up a database that will be used by our factory to copy data from. The Wide World Importers sample database is available at: https://github.com/Microsoft/sql-server-samples/releases/tag/wide-world-importers-v1.0.

A BACPAC is a file that contains database structures and data, similar to a database backup. The difference is that a BACPAC is a snapshot of a database at a specific time. A database backup is much more than that: the database can be restored up to the last few seconds. Also, a database backup can be incremental—that is, contain data and structures since the last backup. A BACPAC always contains all data.

The version we're using is the standard one, as shown in the following screenshot:

We'll now upload the BACPAC to the storage that we created in the previous section:

  1. Open Microsoft Azure Storage Explorer...

Summary

In this chapter, we created our factory with a pipeline that copies data from an Azure SQL database to an Azure storage account. The following chapters will dig more into the various services available in Azure as well as how we can integrate an existing SSIS solution into the factory.

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Key benefits

  • • Combine the power of Azure Data Factory v2 and SQL Server Integration Services
  • • Design and enhance performance and scalability of a modern ETL hybrid solution
  • • Interact with the loaded data in data warehouse and data lake using Power BI

Description

ETL is one of the essential techniques in data processing. Given data is everywhere, ETL will always be the vital process to handle data from different sources. Hands-On Data Warehousing with Azure Data Factory starts with the basic concepts of data warehousing and ETL process. You will learn how Azure Data Factory and SSIS can be used to understand the key components of an ETL solution. You will go through different services offered by Azure that can be used by ADF and SSIS, such as Azure Data Lake Analytics, Machine Learning and Databrick’s Spark with the help of practical examples. You will explore how to design and implement ETL hybrid solutions using different integration services with a step-by-step approach. Once you get to grips with all this, you will use Power BI to interact with data coming from different sources in order to reveal valuable insights. By the end of this book, you will not only learn how to build your own ETL solutions but also address the key challenges that are faced while building them.

Who is this book for?

This book is for you if you are a software professional who develops and implements ETL solutions using Microsoft SQL Server or Azure cloud. It will be an added advantage if you are a software engineer, DW/ETL architect, or ETL developer, and know how to create a new ETL implementation or enhance an existing one with ADF or SSIS.

What you will learn

  • Understand the key components of an ETL solution using Azure Data Factory and Integration Services
  • Design the architecture of a modern ETL hybrid solution
  • Implement ETL solutions for both on-premises and Azure data
  • Improve the performance and scalability of your ETL solution
  • Gain thorough knowledge of new capabilities and features added to Azure Data Factory and Integration Services

Product Details

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Publication date : May 31, 2018
Length: 284 pages
Edition : 1st
Language : English
ISBN-13 : 9781789130096
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Product Details

Publication date : May 31, 2018
Length: 284 pages
Edition : 1st
Language : English
ISBN-13 : 9781789130096
Category :
Concepts :
Tools :

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Table of Contents

7 Chapters
The Modern Data Warehouse Chevron down icon Chevron up icon
Getting Started with Our First Data Factory Chevron down icon Chevron up icon
SSIS Lift and Shift Chevron down icon Chevron up icon
Azure Data Lake Chevron down icon Chevron up icon
Machine Learning on the Cloud Chevron down icon Chevron up icon
Introduction to Azure Databricks Chevron down icon Chevron up icon
Reporting on the Modern Data Warehouse Chevron down icon Chevron up icon

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Amazon Customer Dec 22, 2018
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good
Amazon Verified review Amazon
Marie Conti Sep 27, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Als Hands-On kann man durch die praktischen Beispiele mit Screenshots direkt einsteigen. Die farblichen Grafiken kann man auf der Website zum Buch herunterladen. Kleines Manko: ich hätte mir an manchen Stellen mehr Tiefe gewünscht; bei weiterführenden Themen kommt man ohne die Doku von Microsoft nicht aus. Wer sich aber einen schnellen, praxisorientierten Einstieg verschaffen möchte, ist hier richtig.
Amazon Verified review Amazon
Mario Anzaldua Aug 23, 2018
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Yes, this book is written for the Azure Data Factory (ADF) beginner. It is filled with detailed instructions on how to work with the various Azure modules/services to perform data warehousing activities. The book contains ~275 pages of DW background and setup information across the introduction and 6 project chapters. Each chapter covers nearly every step necessary to complete a specific project but I would recommend that readers have a minimum level of competence with Azure. Readers should be comfortable navigating the various Azure services--or at least Azure SQL db, Storage and Data Factory. You might want to work through a few tutorials so that you can follow along with the book examples. The book contains numerous screen-shots that duplicate the detailed configurations in each of the Azure service blades. Chapter 5 contains a nice intro to Azure Machine Learning that stands on its own.I was happy to find that every exercise in the book succeeded. Following the instructions in each chapter, I ran my jobs to success. Now, that said, I did run into a few challenges. Chapter 4, Azure Data Lake, requires a Service Principal Id and Authentication. The book points readers to Microsoft documentation for this. I spent a day online before getting this to work. I also ran into a couple of hurdles with delimiters in the sample data. One dataset is .csv and has a City, ST field that needs to be worked around for proper processing. You will have to tweak the DataBricks queries a bit. Easy enough to fix but beware that your solution must preserve this column in tact as you will be joining on this field in the final PowerBI report for Chapter 7.Hands-on Data Warehousing is full of useful tips/tricks/steps that get you going with ADF v2. It was great for me as a beginner and saved many hours (days) of online tutorials and research. I finished the book in 1 week. If you need a jump-start on Data Factory, this does the job and the few hurdles keep you aware of what you are trying to achieve. The last chapter brings all your effort together for a nice finish. 4-Stars.
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KingDragonfly Apr 16, 2019
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Like a lot of Azure books, this one's a little shorter than I'd like. It was let down on its Machine Learning section, which somehow was too broad and too specific at the same time. Its target audience seems to be mathematicians. This section should have probably been removed, and expanded into its own book. For those wanting to learn machine learning, search Google for "Machine learning algorithm cheat sheet for Microsoft Azure Machine Learning Studio." Otherwise the rest of the book was good, in particular its mention of how to use existing SSIS packages.
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Sean Forgatch Aug 11, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
This book provides a good overview of some data warehousing related tools in Azure, it does not go in depth with any particular took and provides a 100 level overview. If you are just starting out this is a good choice for you, if your experienced at all with Azure it will not bring much value.
Amazon Verified review Amazon
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