Azure Data Engineering Cookbook

Over 90 recipes to help you orchestrate modern ETL/ELT workflows and perform analytics using Azure services more easily

▶What You Will Learn
-Use Azure Blob storage for storing large amounts of unstructured data
-Perform CRUD operations on the Cosmos Table API
-Implement elastic pools and business continuity with Azure SQL Database
-Ingest and analyze data using Azure Synapse Analytics
-Develop Data Factory data flows to extract data from multiple sources
-Manage, maintain, and secure Azure Data Factory pipelines
-Process streaming data using Azure Stream Analytics and Data Explorer

▶Key Features
-Build highly efficient ETL pipelines using the Microsoft Azure Data services
-Create and execute real-time processing solutions using Azure Databricks, Azure Stream Analytics, and Azure Data Explorer
-Design and execute batch processing solutions using Azure Data Factory

▶Who This Book Is For
This book is for Data Engineers, Database administrators, Database developers, and extract, load, transform (ETL) developers looking to build expertise in Azure Data engineering using a recipe-based approach. Technical architects and database architects with experience in designing data or ETL applications either on-premise or on any other cloud vendor who wants to learn Azure Data engineering concepts will also find this book useful. Prior knowledge of Azure fundamentals and data engineering concepts is needed.

▶What this book covers
- Chapter 1, Working with Azure Blob Storage, covers how to work with Azure Blob storage and understand how it is used when orchestrating a data workflow.

- Chapter 2, Working with Relational Databases in Azure, explains how to provision and work with Azure SQL Database.

- Chapter 3, Analyzing Data with Azure Synapse Analytics, describes how to provision an Azure Synapse database and ingest and analyze data in Azure Synapse.

- Chapter 4, Control Flow Activities in Azure Data Factory, explains how to implement different control activities available in Azure Data Factory.

- Chapter 5, Control Flow Transformation and the Copy Data Activity in Azure Data Factory, explains how to work with the Azure Data Factory integration runtime. You'll also learn to use the SSIS package with Azure Data Factory.

- Chapter 6, Data Flow in Azure Data Factory, explains how to use Azure Data Factory mapping and wrangling data flow to extract, transform, and load data.

- Chapter 7, Azure Data Factory Integration Runtime, details the different integration runtimes available and their use cases.

- Chapter 8, Deploying Azure Data Factory Pipelines, describes how to manually and automatically deploy Azure Data Factory pipelines using the Azure portal and Azure DevOps, respectively.

- Chapter 9, Batch and Streaming Data Processing with Azure Databricks, covers recipes to perform batch and streaming data processing using Azure Databricks.