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

Modeling a data lake

A data lake is, in essence, nothing more than a limitless hard drive that we use to store files. Everyone knows that if you do not carefully consider a folder structure to use on your personal computer, you will end up with a mess and it will become almost impossible to find your files. It is no different for your data lake. Although we cannot speak of data modeling when designing a data lake, creating a structure is really important for a successful implementation. That is even more true when the data in the data lake is made available to end users, such as data analysts working with Power BI or data scientists working with Python notebooks.

When creating a folder structure, you need to take the following things into account:

  • The source of the data
  • The functional meaning of the data
  • Security: Who may or may not need to have access to the data?
  • Who and which applications will use the data?
  • Is the data stored permanently or temporarily...