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Azure Data Factory Cookbook

Azure Data Factory Cookbook

By : Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton
4.9 (29)
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Azure Data Factory Cookbook

Azure Data Factory Cookbook

4.9 (29)
By: Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton

Overview of this book

This new edition of the Azure Data Factory book, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF. There are updated and new recipes throughout the book based on developments happening in Azure Synapse, Deployment with Azure DevOps, and Azure Purview. The current edition also runs you through Fabric Data Factory, Data Explorer, and some industry-grade best practices with specific chapters on each. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out the solutions to them. 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 with its latest advancements as the main ETL and orchestration tool for your data warehouse projects.
Table of Contents (15 chapters)
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13
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Index

Building data model in Delta Lake and data pipeline jobs with Databricks

Apache Spark is a well-known big data framework that is often used for big data ETL/ELT jobs and machine learning tasks. ADF allows us to utilize its capabilities in two different ways:

  1. Running Spark in an HDInsight cluster
  2. Running Databricks notebooks and JAR and Python files

Running Spark in an HDInsight cluster is very similar to the previous recipe. So, we will concentrate on the Databricks service. It also allows running interactive notebooks, which significantly simplifies the development of the ETL/ELT pipelines and machine learning tasks.In this recipe, we will connect Azure Data Lake Storage to Databricks, ingest the MovieLens dataset, transform the data, and store the resulting dataset as delta table in Azure Data Lake Storage.

Getting ready

First, log in to your Microsoft Azure account.We assume you have a pre-configured resource group and storage account with Azure Data Lake Gen2 and the Azure Databricks...

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