Design and develop Azure Cosmos DB database solutions
At a glance
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This learning path builds the skills to design and develop applications on Azure Cosmos DB for NoSQL. You start by evaluating whether the service fits an application, creating your first account and container, and configuring the throughput and consistency settings a workload needs.
You then connect from code: initializing the SDK with a client singleton, performing point operations and transactional batches, and writing queries whose request unit cost you can predict. With that foundation in place, you design a data model and partitioning strategy, which is the decision that most determines how an Azure Cosmos DB application scales.
The path closes with two capabilities that AI applications depend on. You process the change feed to build event-driven integrations and keep denormalized data in sync, then select and apply the AI-assisted development tools that connect an assistant to your data and give an agent durable memory.
Prerequisites
- Experience building cloud applications with C# or Python
- Basic familiarity with JSON and the Azure portal
Achievement Code
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Modules in this learning path
Evaluate whether Azure Cosmos DB for NoSQL is right for your application, including AI workloads, and create your first account, database, container, and items.
Evaluate request-unit, throughput, and consistency needs in Azure Cosmos DB for NoSQL. Configure provisioned or serverless capacity, standard or autoscale throughput, consistency levels, and time to live. Balance cost and performance through account and container settings.
Initialize and configure the Azure Cosmos DB SDK for .NET and Python. Connect with a client singleton and tune connectivity mode. Enable offline development, and handle connection errors to build reliable data access.
Read and write data with the Azure Cosmos DB for NoSQL SDK. Use point reads and the create, replace, upsert, and delete operations. Apply partial document patch, item time to live, optimistic concurrency, transactional batch, and bulk operations. Build correct, efficient data layers in C# and Python.
Author Azure Cosmos DB for NoSQL queries using parameters, projections, and built-in functions, reach into nested data with correlated subqueries and cross-product queries, and page large result sets with continuation tokens in C# and Python.
Design data models for Azure Cosmos DB for NoSQL based on your access patterns. Select partition keys that scale and distribute the workload evenly. Choose an appropriate hierarchical, synthetic, or multitenant partitioning strategy. Avoid costly design anti-patterns.
Read the Azure Cosmos DB for NoSQL change feed with the processor and pull models. Choose the change feed mode a workload needs, react to changes with Azure Functions, and move container data with container copy jobs.
Connect an AI assistant to Azure Cosmos DB with a Model Context Protocol server, add durable memory and multi-step retrieval with the agent toolkits, choose the right tool for a scenario, and separate what each tool covers from what you design yourself.