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ETL with Azure Cookbook

You're reading from   ETL with Azure Cookbook Practical recipes for building modern ETL solutions to load and transform data from any source

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Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781800203310
Length 446 pages
Edition 1st Edition
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Authors (3):
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Christian Cote Christian Cote
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Christian Cote
Matija Lah Matija Lah
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Matija Lah
Madina Saitakhmetova Madina Saitakhmetova
Author Profile Icon Madina Saitakhmetova
Madina Saitakhmetova
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Toc

Table of Contents (12) Chapters Close

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

Moving and transforming data

We copied data in our storage account in the previous recipe, Copying data from the internet. In this recipe, we will join and transform the two datasets using a mapping data flow. They contain many tools that help us join, map, and aggregate our source datasets to produce another output (sink) dataset.

Mapping dataflows are a new addition for Data Factory. They were not part of the initial Data Factory v2 that was available in July 2018. The ADF team added the mapping data flows about a year later. For those who are familiar with SSIS, they will understand how to work with them in no time.

So, let's dig into our recipe!

Getting ready

This recipe assumes that you have access to an Azure subscription. It can be a free trial one as described in the recipe Creating an Azure subscription, in Chapter 1, Getting Started with Azure and SSIS 2019. It also assumes that you have created some Data Factory artifacts such as linked services, datasets...

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