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)

Tracing SQL queries for dedicated SQL pool to Synapse integration pipelines

In the Using Kusto queries to monitor SQL and Spark pools recipe of Chapter 11, Monitoring Synapse SQL and Spark Pools, we explored using Kusto queries and a Log Analytic workspace to find expensive queries in a dedicated SQL pool. However, in data engineering projects, finding the expensive queries in a dedicated SQL pool alone wouldn’t be sufficient as you need to find the details about the integration pipeline that fired the query. To do this, we need to find a way to correlate the Log Analytics data from the integration pipelines and a dedicated SQL pool.

Fortunately, Copy activity in an integration pipeline automatically adds a label to the SQL query it uses to copy the data. We can easily identify the pipeline and activity name from the label attached to the SQL query in the dedicated SQL pool. However, other activities, such as data flows and SQL stored procedure tasks, don’t automatically...