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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

Implementing a Data Lake Analytics U-SQL activity

Azure Data Lake Analytics is an on-demand analytics service that allows you to process data using R, Python, and U-SQL without provisioning any infrastructure. All we need to do is to upload the data onto Data Lake, provision the Data Lake Analytics account, and run U-SQL to process the data.

In this recipe, we'll implement a Data Lake Analytics U-SQL activity to calculate total sales by country from the orders data stored in the Data Lake store.

Getting ready

To get started, do the following:

  1. Log in to https://portal.azure.com using your Azure credentials.
  2. Open a new PowerShell prompt. Execute the Connect-AzAccount command to log in to your Azure account from PowerShell.
  3. You will need an existing Data Factory account. If you don't have one, create one by executing the ~/azure-data-engineering-cookbook\Chapter04\3_CreatingAzureDataFactory.ps1 PowerShell script.

How to do it…

Let&apos...

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