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Azure Data Factory Cookbook

Azure Data Factory Cookbook

By : Dmitry Anoshin, Dmitry Foshin, Storchak, Xenia Ireton
4.2 (13)
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Azure Data Factory Cookbook

Azure Data Factory Cookbook

4.2 (13)
By: Dmitry Anoshin, Dmitry Foshin, Storchak, Xenia Ireton

Overview of this book

Azure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you’ll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you’ll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects.
Table of Contents (12 chapters)
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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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