Now that we've created our cluster and folder, we must prepare some data to work with. For this book, we're using the data warehouse data available in the on-premise SQL database we created in the first chapters. This will allow us to see another integration runtime: self-hosted. We'll copy the data in the Azure storage account created previously in this chapter.
Hands-On Data Warehousing with Azure Data Factory
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Hands-On Data Warehousing with Azure Data Factory
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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.
Table of Contents (8 chapters)
Preface
Free Chapter
The Modern Data Warehouse
Getting Started with Our First Data Factory
SSIS Lift and Shift
Azure Data Lake
Machine Learning on the Cloud
Introduction to Azure Databricks
Reporting on the Modern Data Warehouse
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