MSDN Documentation

Azure Cosmos DB Data Modeling Tutorials

Master the art of designing efficient and scalable data models for your Azure Cosmos DB solutions. This section provides a curated collection of tutorials to guide you through best practices, common patterns, and advanced techniques.

Featured Tutorials

• Introduction to Data Modeling in Azure Cosmos DB - Understand the fundamentals of document, key-value, graph, and column-family data models and how they apply to Cosmos DB.
• Modeling Relational Data in Azure Cosmos DB - Learn strategies for representing relational data structures in a NoSQL document database, including denormalization techniques.
• Modeling Graph Data with Azure Cosmos DB (Gremlin API) - Explore how to leverage the Gremlin API to build powerful graph databases and model complex relationships.
• Effective Partitioning Strategies for Performance - Dive deep into partition key selection and understand how it impacts throughput, scalability, and cost.
• Advanced Modeling Patterns and Anti-Patterns - Discover advanced techniques like embedding, referencing, and explore common pitfalls to avoid.
• Data Modeling for IoT Scenarios - Specific guidance on modeling high-volume, time-series data typical in Internet of Things applications.

Key Concepts Covered

• Understanding the four Azure Cosmos DB APIs: SQL (Core), MongoDB, Cassandra, Gremlin, and Table.
• Choosing the right API for your workload.
• Normalization vs. Denormalization in a NoSQL context.
• Embedding and referencing patterns.
• Designing efficient partition keys.
• Handling relationships between documents.
• Querying optimized data models.
• Cost considerations in data modeling.

Practical Examples

Each tutorial includes practical code samples and step-by-step instructions using the Azure Cosmos DB SQL API (Core) by default, with notes on adapting them for other APIs where relevant.