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)

Processing Data Using Azure Databricks

Databricks is a data engineering product built on top of Apache Spark that provides a unified, cloud-optimized platform so that you can perform Extract, Transform, and Load (ETL), Machine Learning (ML), and Artificial Intelligence (AI) tasks on a large quantity of data.

Azure Databricks, as its name suggests, is the Databricks integration with Azure, which also provides fully managed Spark clusters, an interactive workspace for data visualization and exploration, integration with data sources such as Azure Blob Storage, Azure Data Lake Storage, Azure Cosmos DB, and Azure SQL Data Warehouse.

Azure Databricks can process data from multiple and diverse data sources, such as SQL or NoSQL, structured or unstructured data, and streaming data sources, and also scale up as many servers as required to cater to any data growth.

By the end of the chapter, you will have learned how to configure Databricks, work with storage accounts, process data...