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

Using a Kimball data warehouse versus data marts

The starting point of creating a star schema is choosing a process to model. One star model describes one process. A business is always more than a single process. In the theory of Ralph Kimball, a data warehouse is the collection of all star schemas that together describe the entire organization.

There will always be overlap between the individual star schemas you create to model the individual processes. The processes are not completely independent of each other. The sales department sells products that the purchasing department buys. They work with the same products. So, the star schema describing the sales process will have the dimProduct and dimDate dimensions in common with the star for purchasing. They will have different dimensions as well. The star schema for purchasing might have a dimension for suppliers, whereas the sales start schema probably has a dimension for customers.

When you design the dimProduct dimension table...