Book Image

Cloud Scale Analytics with Azure Data Services

By : Patrik Borosch
Book Image

Cloud Scale Analytics with Azure Data Services

By: Patrik Borosch

Overview of this book

Azure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs.
Table of Contents (20 chapters)
1
Section 1: Data Warehousing and Considerations Regarding Cloud Computing
4
Section 2: The Storage Layer
7
Section 3: Cloud-Scale Data Integration and Data Transformation
14
Section 4: Data Presentation, Dashboarding, and Distribution

Understanding other SQL options in Azure

In Chapter 2, Connecting Requirements and Technology, we talked about different size options for your modern data warehouse. When we look at the M size, we have added an Azure SQL database for the presentation layer. When we compare the Azure SQL database to Synapse Analytics databases, the main difference is the SMP character of the Azure SQL database. See also Figure 4.1 for a comparison of SMP versus MPP.

In a SQL database, data is, by default, stored in a row orientation as it is done in SQL Server. In general, you can think of the Azure SQL database as a single database that you would spin up and use. Almost all of the functionality of a SQL Server database is available with an Azure SQL database as well.

You will also have the option to create CCIs on your tables. This will give you high analytical performance on your data stored there. In comparison to Synapse Analytics, you won't get the same scale-out architecture with...