For many newcomers to Cosmos DB, the learning process starts with data modeling and partitioning. How should you structure your model? When should you combine multiple entity types in a single container? Should you de-normalize your entities? What's the best partition key for your data?
In this session, we discuss the key strategies for modeling and partitioning data effectively in Cosmos DB. Using a real-world NoSQL example based on the AdventureWorks relational database, we explore key Cosmos DB concepts—request units (RUs), partitioning, and data modeling—and how their understanding guides the path to a data model that yields the best performance and scalability. Attend this session, and acquire the critical skills you'll need to design the optimal database for Cosmos DB.
You will learn:
- Translate traditional relational database concepts to modern NoSQL data modeling techniques
- Understand the tradeoffs in embedding vs. referencing, and normalizing vs. de-normalizing
- How to develop the optimal database in terms of scale, performance, and cost