Book Image

Azure Data Engineering Cookbook

By : Ahmad Osama
Book Image

Azure Data Engineering Cookbook

By: Ahmad Osama

Overview of this book

Data engineering is one of the faster growing job areas as Data Engineers are the ones who ensure that the data is extracted, provisioned and the data is of the highest quality for data analysis. This book uses various Azure services to implement and maintain infrastructure to extract data from multiple sources, and then transform and load it for data analysis. It takes you through different techniques for performing big data engineering using Microsoft Azure Data services. It begins by showing you how Azure Blob storage can be used for storing large amounts of unstructured data and how to use it for orchestrating a data workflow. You'll then work with different Cosmos DB APIs and Azure SQL Database. Moving on, you'll discover how to provision an Azure Synapse database and find out how to ingest and analyze data in Azure Synapse. As you advance, you'll cover the design and implementation of batch processing solutions using Azure Data Factory, and understand how to manage, maintain, and secure Azure Data Factory pipelines. You’ll also design and implement batch processing solutions using Azure Databricks and then manage and secure Azure Databricks clusters and jobs. In the concluding chapters, you'll learn how to process streaming data using Azure Stream Analytics and Data Explorer. By the end of this Azure book, you'll have gained the knowledge you need to be able to orchestrate batch and real-time ETL workflows in Microsoft Azure.
Table of Contents (11 chapters)

Optimizing queries using materialized views in Azure Synapse Analytics

Views are an old concept in SQL Server and are often used to encapsulate complex queries into virtual tables. We can then replace the query with the virtual table wherever required. A standard view is just a name given to the complex query. Whenever we query the standard view, it accesses the underlying tables in the query to fetch the result set.

Materialized views, unlike standard views, maintain the data as a physical table instead of a virtual table. The view data is maintained just like a physical table and is refreshed automatically whenever the underlying tables are updated.

In this recipe, we'll learn how to optimize queries using materialized views.

Getting ready

Before you start, open SSMS and log in to Azure SQL Server.

You need a Synapse SQL pool to perform the steps in this recipe. If you don't have an existing Synapse SQL pool, you can create one using the steps from the...