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

Understanding SQL Server data types

The conceptual data model we have created thus far defines what tables a database consists of. Also, it tells you all the columns that form each table. For instance, a table called Product may contain the ProductName, ListPrice, Category, and EndDate columns. Before we can actually create this table, we need to know what sort of values each column will contain. In other words, we need to choose an appropriate data type. The data type of a column determines three things:

  • Which types of values can be stored in the column. Can you store numbers, such as product prices, or dates, such as transaction dates?
  • What computations or manipulations you can do with the data. For example, you can add two numbers together, but you cannot multiply two dates.
  • The efficiency of both data storage and data manipulations. Some data types are smaller than others, making them more efficient to store and work with.

Choosing the proper data type is...