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Azure Data Engineering Cookbook

You're reading from   Azure Data Engineering Cookbook Design and implement batch and streaming analytics using Azure Cloud Services

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800206557
Length 454 pages
Edition 1st Edition
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Authors (2):
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Nagaraj Venkatesan Nagaraj Venkatesan
Author Profile Icon Nagaraj Venkatesan
Nagaraj Venkatesan
Ahmad Osama Ahmad Osama
Author Profile Icon Ahmad Osama
Ahmad Osama
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Toc

Table of Contents (11) Chapters Close

Preface 1. Chapter 1: Working with Azure Blob Storage 2. Chapter 2: Working with Relational Databases in Azure FREE CHAPTER 3. Chapter 3: Analyzing Data with Azure Synapse Analytics 4. Chapter 4: Control Flow Activities in Azure Data Factory 5. Chapter 5: Control Flow Transformation and the Copy Data Activity in Azure Data Factory 6. Chapter 6: Data Flows in Azure Data Factory 7. Chapter 7: Azure Data Factory Integration Runtime 8. Chapter 8: Deploying Azure Data Factory Pipelines 9. Chapter 9: Batch and Streaming Data Processing with Azure Databricks 10. Other Books You May Enjoy

Chapter 6: Data Flows in Azure Data Factory

In this chapter, we'll look at two data flow activities: the mapping data flow and the wrangling data flow. Data flow activities provide a code-free way to implement transformations on the fly as and when data is being processed.

Incremental data loading is a very common scenario wherein data from a source is incrementally loaded to a destination. There are multiple ways to implement incremental data flows. This chapter provides an implementation of incremental data loading that you can use in your environment as and when required.

The wrangling data flow provides a code-free UI that can be used to clean and transform data using Power Query. This makes it easy for non-developers to implement data transformation and cleaning and create data pipelines quickly.

In this chapter, we'll cover the following recipes:

  • Implementing incremental data loading with a mapping data flow
  • Implementing a wrangling data flow

    Note...

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