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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 9: Data Vault Modeling

In Chapter 7, Dimensional Modeling, you learned how to create a star schema database. In Chapter 8, Provisioning and Implementing an Azure Synapse SQL Pool, you implemented a star schema. The goal was to optimize the database for analysis and reporting.

Demands and regulations change over time, meaning that the way we use data also changes over time. These changes the way we use data for us to implement changes in the star schema designs we create. To create a stable data platform, we implement a layer in between the operational databases that we learned about in chapters 3 to 6, and the dimensionally modeled databases that we learned about in Chapter 7, Dimensional Modeling, and Chapter 8, Provisioning and Implementing an Azure Synapse SQL Pool. This layer should be optimized for the long-term, flexible storage of historical data. When data here means relational data, creating a data vault data warehouse is a good option. That is what this chapter...

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