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Data Modeling for Azure Data Services

Data Modeling for Azure Data Services

By : Braake
4.8 (16)
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Data Modeling for Azure Data Services

Data Modeling for Azure Data Services

4.8 (16)
By: 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)
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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

Designing fact tables

Dimension tables may be important, but the facts are what really matter. So, before going into fact tables, let's look at the facts first. We already learned about facts in the dimensional modeling section, but we need to look at facts in a bit more detail. When we do, we can distinguish three types of facts:

  • Additive facts
  • Semi-additive facts
  • Non-additive facts

We will discuss them in turn.

Understanding additive facts

Additive facts are numerical facts that you can add together to create facts at a higher aggregation level. Almost all reports use aggregated facts. For instance, a report showing sales by month is an aggregated report. All individual sales transactions of the same month are put together to form one new row. The sales are calculated by adding all the sales amounts together to form the month's sales. The overall sales are then calculated by adding all the sales amounts of all the months together.

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