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)

Optimizing query performance in Synapse Spark pools

There are several methods you can use to optimize the performance of queries in a lake database, such as caching, indexing, partitioning, Z-ordering, data skipping, and using query hints. This recipe will showcase the following two methods to optimize the performance of a query:

  • Z-ordering: Z-ordering helps the Spark engine easily locate columns with the same value
  • Partitioning: Partitioning will partition the Delta lake table into smaller chunks, creating subfolders in the data lake storage account for each distinct value on the partitioned column

Getting ready

To get started, log into https://portal.azure.com using your Azure credentials.

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

Create a Spark pool cluster, as explained in the Provisioning and configuring Spark pools recipe...