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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

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781803246598
Length 532 pages
Edition 2nd Edition
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Authors (4):
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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
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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

Introduction

A data lake is a central storage system that stores data in its raw format. It is used to collect huge amounts of data that are yet to be analyzed by analysts and data scientists or for regulatory purposes. As the amount of information and the variety of data that a company operates with increases, it gets increasingly difficult to preprocess and store it in a traditional data warehouse. By design, data lakes are built to handle unstructured and semi-structured data with no pre-defined schema.On-premise data lakes are difficult to scale and require thorough requirements and cost estimations. Cloud data lakes are often considered an easier-to-use and -scale alternative. In this chapter, we will go through a set of recipes that will help you to launch a data lake, load data from external storage, and build ETL/ELT pipelines around it.Azure Data Lake Gen2 can store both structured and unstructured data. In this chapter, we will load and manage our datasets in Azure Data Lake...

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