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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

Summary

Data Vault modeling is a mix between normalizing data and dimensional modeling. It is designed to provide a flexible way to store detailed, historical data. By using Hubs, Links, and Satellites, you create a database in which you never need to alter an existing table. All changes can be handled by adding new tables. That makes a Data Vault more stable over time than other database designs.

Data Vault is also very standardized. This enables tools to automatically generate Data Vault structures from existing normalized databases. On top of that, the ETL process that loads the tables can also be generated for the most part.

As a third benefit, Data Vault is scalable. With the possibility to load all tables in parallel, it can leverage powerful hardware and load vast amounts of data in short periods of time.

Using the business vault functionalities improves the usability of data. However, using Data Marts behind the Data Vault is still a best practice.

A Data Vault...

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