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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

Integrating separate results

We analyzed two different reports in the two previous sections of this chapter. Both reports provide information on the same project organization and show data coming from the same database. The results of the independently analyzed reports need to be integrated into a single database schema.

During integration, we merge the result of normalizing the information requirements. We need to pay attention to the following:

  • Tables that describe the same entity
  • Homonyms and synonyms
  • Process data

Tables that are the same

An entity (a table) is defined by its key. So, we first look for tables with the same key. We can merge these tables.

Both in Figure 3.7 and in Figure 3.15, we see a table called Project. The fact that we chose the same name is irrelevant, just a coincidence. The fact that both tables have the same key, ProjectNumber, means that they are actually the same table. You merge them by creating a table that contains all...

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