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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

Chapter 7: Dimensional Modeling

Normalizing data is not always the best strategy when designing a relational database. We already mentioned several times that normalizing data is beneficial for an OLTP workload. OLTP workloads are workloads of primary processes, that is, of line-of-business processes.

Databases normalized to the third normal form turned out to be bad for query performance when we started doing more analytical queries on the data. Dimensional modeling came up as an alternative method for designing database table structures. Dimensional modeling leads to a database design optimized for analytics. For instance, the resulting star schema is the ideal table structure for Power BI.

This chapter is all about dimensional modeling and the resulting star schemas. We will learn about the following topics:

  • Background to dimensional modeling
  • Steps to get to a star schema database model
  • Designing dimension tables
  • Designing fact tables
  • Using a Kimball...
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