Book Image

Data Modeling for Azure Data Services

By : Peter ter Braake
Book Image

Data Modeling for Azure Data Services

By: Peter ter Braake

Overview of this book

Data is at the heart of all applications and forms the foundation of modern data-driven businesses. With the multitude of data-related use cases and the availability of different data services, choosing the right service and implementing the right design becomes paramount to successful implementation. Data Modeling for Azure Data Services starts with an introduction to databases, entity analysis, and normalizing data. The book then shows you how to design a NoSQL database for optimal performance and scalability and covers how to provision and implement Azure SQL DB, Azure Cosmos DB, and Azure Synapse SQL Pool. As you progress through the chapters, you'll learn about data analytics, Azure Data Lake, and Azure SQL Data Warehouse and explore dimensional modeling, data vault modeling, along with designing and implementing a Data Lake using Azure Storage. You'll also learn how to implement ETL with Azure Data Factory. By the end of this book, you'll have a solid understanding of which Azure data services are the best fit for your model and how to implement the best design for your solution.
Table of Contents (16 chapters)
1
Section 1 – Operational/OLTP Databases
8
Section 2 – Analytics with a Data Lake and Data Warehouse
13
Section 3 – ETL with Azure Data Factory

Preface

Databases play an important role in almost all the applications that we use. The database has a direct impact on the performance and scalability of the application it supports. That makes choosing the right type of database to use and designing that database correctly a vital part of all development since scalability and performance depend on a well-chosen design. With databases hosted in Azure, the design may also have a direct impact on the costs of the database. The first part of this book teaches you when to use a relational database (Azure SQL Database) and when a NoSQL database (Cosmos DB) is the better option. You will also learn how to design that database and, finally, how to implement the chosen design.

All the data gathered by applications can be used for Business Intelligence (BI). A crucial part of BI is creating a central repository for your data to build your BI solution on. This can be a data lake with data marts, or it may be a data warehouse. In the second part of the book, you will learn to design data warehouses according to the theory of dimensional modeling or by designing a data vault. You will also learn how to design a data lake. You will then learn how to apply what you have learned by creating a data lake in Azure Storage and creating data marts using Azure Synapse Analytics.

The book ends with a chapter on Azure Data Factory. Data Factory is used to get data from the source databases you created in part one and store that data in the data platforms you implemented in part two.

After reading this book, you have a solid understanding of data modeling and of how to implement a database schema in Azure. This will help you to build scalable and cost-effective data solutions in Azure.