Expanded Library

Data Modeling and Partitioning Patterns in Azure Cosmos DB

by Leonard Lobel

This course targets data professionals that need to learn data modeling strategies for Azure Cosmos DB, and how they differ from traditional methodologies.

What you'll learn

While Azure Cosmos DB is easy to use, it’s very different compared to a traditional relational database. In this course, Data Modeling and Partitioning Patterns in Azure Cosmos DB, you’ll learn how to design effective data models for Cosmos DB, Microsoft’s horizontally partitioned, non-relational database platform on Azure. First, you’ll explore the step-by-step process of adapting a relational schema to a data model optimized for Cosmos DB based on the familiar AdventureWorks sample database. Next, you’ll discover core concepts such as partitioning and throughput needed to get your job done. Finally, you’ll delve into non-relational data modeling practices, like embedding vs. referencing, schema-free data structures, and data denormalization with the Change Feed API, Azure Functions, and transactionalized stored procedures. By the end of this course, you’ll have the necessary knowledge to achieve the optimal design for your data models in Azure Cosmos DB.

About the author

Leonard Lobel (Microsoft MVP, SQL Server) is CTO and co-founder of Sleek Technologies, Inc., a New York-based development shop. He is also a principal consultant at Tallan, Inc., a Microsoft National Systems Integrator and Gold Competency Partner. Lenni is also a consultant, trainer, and frequent speaker at major industry conferences.

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