Data Modeling and Partitioning Patterns in Azure Cosmos DB

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Data Modeling and Partitioning Patterns in Azure Cosmos DB
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 47m | 246 MB
Instructor: 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.

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.

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