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Azure Data Factory Cookbook

You're reading from   Azure Data Factory Cookbook Build and manage ETL and ELT pipelines with Microsoft Azure's serverless data integration service

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781800565296
Length 382 pages
Edition 1st Edition
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Authors (4):
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Dmitry Anoshin Dmitry Anoshin
Author Profile Icon Dmitry Anoshin
Dmitry Anoshin
Roman Storchak Roman Storchak
Author Profile Icon Roman Storchak
Roman Storchak
Xenia Ireton Xenia Ireton
Author Profile Icon Xenia Ireton
Xenia Ireton
Dmitry Foshin Dmitry Foshin
Author Profile Icon Dmitry Foshin
Dmitry Foshin
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Toc

Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Getting Started with ADF 2. Chapter 2: Orchestration and Control Flow FREE CHAPTER 3. Chapter 3: Setting Up a Cloud Data Warehouse 4. Chapter 4: Working with Azure Data Lake 5. Chapter 5: Working with Big Data – HDInsight and Databricks 6. Chapter 6: Integration with MS SSIS 7. Chapter 7: Data Migration – Azure Data Factory and Other Cloud Services 8. Chapter 8: Working with Azure Services Integration 9. Chapter 9: Managing Deployment Processes with Azure DevOps 10. Chapter 10: Monitoring and Troubleshooting Data Pipelines 11. Other Books You May Enjoy

Chapter 5: Working with Big Data – HDInsight and Databricks

Azure Data Factory (ADF) is known for its efficient utilization of big data tools. This allows building fast and scalable ETL/ELT pipelines and easily managing the storage of petabytes of data. Often, setting up a production-ready cluster used for data engineering jobs is a daunting task. On top of this, estimating loads and planning for an autoscaling capacity can be tricky. Azure with HDInsight clusters and Databricks make these tasks obsolete. Now, any Azure practitioner can set up an Apache Hive, Apache Spark, or Apache Kafka cluster in minutes.

In this chapter, we are going to cover the following recipes that will help build your ETL infrastructure:

  • Setting up an HDInsight cluster
  • Processing data from Azure Data Lake with HDInsight and Hive
  • Processing big data with Apache Spark
  • Building a machine learning app with Databricks and Azure Data Lake Storage
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