Data Modeling for Azure Data Services

• Design a cost-effective, performant, and scalable database in Azure
• Choose and implement the most suitable design for a database
• Discover how your database can scale with growing data volumes, concurrent users, and query complexity

Book DescriptionData 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.

What you will learn

• Model relational database using normalization, dimensional, or Data Vault modeling
• Provision and implement Azure SQL DB and Azure Synapse SQL Pools
• Discover how to model a Data Lake and implement it using Azure Storage
• Model a NoSQL database and provision and implement an Azure Cosmos DB
• Use Azure Data Factory to implement ETL/ELT processes
• Create a star schema model using dimensional modeling

Who this book is forThis book is for business intelligence developers and consultants who work on (modern) cloud data warehousing and design and implement databases. Beginner-level knowledge of cloud data management is expected.