Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Azure Data Factory Cookbook

You're reading from   Azure Data Factory Cookbook Build ETL, Hybrid ETL, and ELT pipelines using ADF, Synapse Analytics, Fabric and Databricks

Arrow left icon
Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781803246598
Length 532 pages
Edition 2nd Edition
Tools
Arrow right icon
Authors (4):
Arrow left icon
Tonya Chernyshova Tonya Chernyshova
Author Profile Icon Tonya Chernyshova
Tonya Chernyshova
Xenia Ireton Xenia Ireton
Author Profile Icon Xenia Ireton
Xenia Ireton
Dmitry Foshin Dmitry Foshin
Author Profile Icon Dmitry Foshin
Dmitry Foshin
Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
Arrow right icon
View More author details
Toc

Table of Contents (15) Chapters Close

Preface 1. Getting Started with ADF 2. Orchestration and Control Flow FREE CHAPTER 3. Setting Up Synapse Analytics 4. Working with Data Lake and Spark Pools 5. Working with Big Data and Databricks 6. Data Migration – Azure Data Factory and Other Cloud Services 7. Extending Azure Data Factory with Logic Apps and Azure Functions 8. Microsoft Fabric and Power BI, Azure ML, and Cognitive Services 9. Managing Deployment Processes with Azure DevOps 10. Monitoring and Troubleshooting Data Pipelines 11. Working with Azure Data Explorer 12. The Best Practices of Working with ADF 13. Other Books You May Enjoy
14. Index

Copying large datasets from S3 to ADLS

Azure Data Factory can help you move very large datasets into the Azure ecosystem with speed and efficiency. The key to moving large datasets is data partitioning. The way you partition depends heavily on the nature of your data.In the following recipe, we will illustrate a methodology to utilize a data partitioning table for moving a large dataset. We will use a public Common Crawl dataset, which contains petabytes of web crawl data from 2008 to the present day. It is a public dataset hosted on the AWS S3 platform. We will only use a small subset of this data for our example, enough to illustrate the power of data factory parallel processing.

Getting ready

In order to access Amazon Web Services, such as an S3 bucket, you need to have proper credentials. These credentials consist of an access key ID (for example, AKFAGOKFOLNN7EXAMPL8) and the secret access key itself (for example, pUgkrUXtPFEer/PO9rbNG/bPxRgiMYEXAMPLEKEY). In this book, we will...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime