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

Introducing Azure Data Factory

In Chapter 8, Provisioning and Implementing an Azure Synapse SQL Pool, you saw the modern data warehouse architecture as shown in Figure 11.1:

Figure 11.1 – Modern data warehouse architecture

All the arrows in Figure 11.1 denote data movement activities. We need to export data from the operational databases and copy that data to the data lake's raw zone. We then process the newly added data and move the now transformed data into the curated zone of the data lake. From there, the data is transformed even more. Business rules are applied and the data is shaped into a star schema design to be stored in a data mart. We might then process an Analysis Services tabular model to serve the data to end users.

The entire process of moving data and transforming it is referred to as the ETL process. It sometimes involves copying data and, on other occasions, the data needs to be transformed. Depending on the situation, we can...