Azure Cosmos DB for .NET Developers: From Document Thinking to Production Patterns - Tapa blanda

Sinopsis

The only current book on Azure Cosmos DB for .NET developers — written by a 20-year Microsoft MVP who built the open-source library used throughout.

You know relational databases. You've spent years writing entity classes, mapping foreign keys, building adapter layers, and wrestling EF Core into submission. It works. But it costs you something — and you feel it every time a simple domain object gets shredded into five tables just so the database can store it.

Cosmos DB stores your objects as-is. No impedance mismatch. No DTO translation layers. No adapter code. Your domain model goes in, your domain model comes out. But thinking in documents instead of rows requires a genuine mental shift — and the official docs won't get you there.

This book will.

What's inside:

• The "trees vs. boxes" mental model that changes how you think about data design
• Document modeling with aggregate roots — where to draw the boundaries and why
• Hierarchical partition keys, request unit economics, and how not to go broke
• The repository pattern, done right, for Cosmos DB
• Change Feed event patterns with Azure Functions
• Security, managed identity, and the two permission systems nobody explains well
• A complete production case study — a cocktail recipe search engine with 5,800 recipes, multi-tenancy, and AI-powered vector search
• LINQ vs. raw SQL query mechanics and how to instrument what actually runs
• Reporting with Microsoft Fabric when Cosmos DB isn't the right tool
• Two reference appendices you'll keep coming back to

All examples use C# and ASP.NET Core. The author's production-tested Benday.CosmosDb NuGet library is used throughout — not as a black box, but as a teaching tool for patterns you can apply to your own code.

Benjamin Day is an independent consultant with nearly 30 years in .NET, Azure, and software architecture. A 20-consecutive-year Microsoft MVP and Pluralsight author, he has helped organizations across industries modernize their data platforms and fix the structural problems that tools alone can't solve.

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