Book Image

Hands-On Data Warehousing with Azure Data Factory

By : Christian Cote, Michelle Gutzait, Giuseppe Ciaburro
Book Image

Hands-On Data Warehousing with Azure Data Factory

By: Christian Cote, Michelle Gutzait, Giuseppe Ciaburro

Overview of this book

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.

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Table of Contents (12 chapters)
Title Page
Title Page
Packt Upsell
Packt Upsell
Contributors
Contributors
Preface
Preface
Free Chapter
1
The Modern Data Warehouse
The Modern Data Warehouse
The need for a data warehouse
The modern data warehouse
What's new in V2.0?
Summary
2
Getting Started with Our First Data Factory
Getting Started with Our First Data Factory
Resource group
Azure Data Factory
Azure Blob storage
Types of blobs
SQL Azure database
Summary
3
SSIS Lift and Shift
SSIS Lift and Shift
SSIS in ADF
Leveraging our package in ADF V2
Summary
4
Azure Data Lake
Azure Data Lake
Creating and configuring Data Lake Store
Creating a Data Lake Analytics resource
Using the data factory to manipulate data in the Data Lake
Run U-SQL from a job in the Data Lake Analytics
Summary
5
Machine Learning on the Cloud
Machine Learning on the Cloud
Machine learning overview
Machine learning tasks
Azure Machine Learning Studio
Breast cancer detection
Summary
6
Introduction to Azure Databricks
Introduction to Azure Databricks
Azure Databricks setup
Prepare the data to ingest
Copy data from SQL Server to sales-data
Databricks notebook
Calling Databricks notebook execution in ADF
Summary
7
Reporting on the Modern Data Warehouse
Reporting on the Modern Data Warehouse
Different types of BI
Power BI consumption
Creating our Power BI reports
Incorporating Spark data
Summary
Index
Index

Creating our Power BI reports

Now that we have explained what Power BI is, it is time to create our first reports with it. The Power BI development life cycle starts with Power BI for desktop. We create our reports with it and then publish them on the cloud or on-premise on our local Power BI Report Servers.

This chapter will focus on two paradigms of BI as explained in the previous sections: personal and team BI.

Reporting with on-premise data sources

Our first Power BI report will use the on-premise World Wide Importers we talked about earlier in this book. We will first connect to the World Wide Importers relational database using Power BI for desktop.

If you haven't created an account yet, create one now; it's free. If you already have a Power BI account, select the link as highlighted in the following screenshot:

Once connected, uncheck Show this screen on startup and click on the Get data.

In the Get Data window, select SQL Server database and click on Connect:

As shown in the following screenshot...

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