Book Image

Azure Data Engineering Cookbook - Second Edition

By : Nagaraj Venkatesan, Ahmad Osama
Book Image

Azure Data Engineering Cookbook - Second Edition

By: Nagaraj Venkatesan, Ahmad Osama

Overview of this book

The famous quote 'Data is the new oil' seems more true every day as the key to most organizations' long-term success lies in extracting insights from raw data. One of the major challenges organizations face in leveraging value out of data is building performant data engineering pipelines for data visualization, ingestion, storage, and processing. This second edition of the immensely successful book by Ahmad Osama brings to you several recent enhancements in Azure data engineering and shares approximately 80 useful recipes covering common scenarios in building data engineering pipelines in Microsoft Azure. You’ll explore recipes from Azure Synapse Analytics workspaces Gen 2 and get to grips with Synapse Spark pools, SQL Serverless pools, Synapse integration pipelines, and Synapse data flows. You’ll also understand Synapse SQL Pool optimization techniques in this second edition. Besides Synapse enhancements, you’ll discover helpful tips on managing Azure SQL Database and learn about security, high availability, and performance monitoring. Finally, the book takes you through overall data engineering pipeline management, focusing on monitoring using Log Analytics and tracking data lineage using Azure Purview. By the end of this book, you’ll be able to build superior data engineering pipelines along with having an invaluable go-to guide.
Table of Contents (16 chapters)

Handling schema changes dynamically in data flows using schema drift

A common challenge in extraction, transformation, and load (ETL) projects is when the schema changes at the source and the pipelines that are supposed to read the data from the source, transform it, and ingest it to the destination, start to fail. Schema drift, a feature in data flows, addresses this problem by allowing us to dynamically define the column mapping in transformations. In this recipe, we will make some changes to the schema of a data source, use schema drift to detect the changes, and handle changes without any manual intervention gracefully.

Getting ready

Create a Synapse Analytics workspace as explained in the Provisioning an Azure Synapse Analytics workspace recipe in Chapter 8, Processing Data Using Azure Synapse Analytics.

Complete the Copying data using a Synapse data flow recipe in this chapter.

How to do it…

In this recipe, we will be using the Copy_CSV_to_Parquet data flow...