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

Background to Data Vault modeling

We have already discussed nomalizing data and dimensional modeling as possible ways to design relational databases. Normalizing is a great strategy for databases that support line-of-business applications. Dimensionally modeled databases are optimized for reporting. In this chapter, we will look at a third alternative – Data Vault modeling. It optimizes the model for the flexible long-term storage of historical data.

Have a look at Figure 9.1, which is a copy of Figure 7.2:

Figure 9.1 – Classic data warehouse architecture

This figure shows how Ralph Kimball envisioned the data warehouse. The data warehouse is a collection of all the star schemas describing an organization. It has a star for Sales, a star for Marketing, a star for Human Resources, and so forth. It is loaded from the data that originates from line-of-business applications. Whenever two processes use the same dimension, you create a conformed...