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  • Format: ePub

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
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Produktbeschreibung
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.


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Autorenporträt
Peter Ter Braake started working as a developer in 1996 after studying physics in Utrecht, the Netherlands. Databases and business intelligence piqued his interest the most, leading to him specializing in SQL Server and its business intelligence components. He has worked with Power BI from the tool's very beginnings. Peter started working as an independent contractor in 2008. This has enabled him to divide his time between teaching data-related classes, consulting with customers, and writing articles and books.