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 of data lakes

Let's start with the definition of a data lake:

A data lake is an environment where you collect and store (vast amounts of) raw data in its original format.

The term data lake comes from a water analogy. You can make money using water, just as you can make money by using data. But you will need to store the water somewhere until you find a use case for it. You don't necessarily know beforehand what that use case is going to be. This means you need a cheap and easy way to store the water. Putting all your water in bottles is optimal when you want to sell it as drinking water. But pouring water out of a bottle over a house that is on fire in the hope of extinguishing the fire would probably be useless. So, you wouldn't bottle it until you started selling drinking water:

Figure 10.1 – Data lake

Data is analogous to water. When you store it for later use, but you don't necessarily know all the (possible) use cases (yet...