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- Learn how to connect to Azure Cosmos DB for NoSQL, use the SDK to perform CRUD operations, and write SQL queries to retrieve document data for AI applications.
After completing this module, you'll be able to:
- Explain the Azure Cosmos DB for NoSQL resource model and how databases, containers, and items relate to each other
- Implement SDK operations to connect to Azure Cosmos DB and perform CRUD operations on items
- Select between point reads and queries based on performance requirements and access patterns
- Build queries using SQL syntax to filter, project, and retrieve data from containers
- Learn how to implement vector search capabilities in Azure Cosmos DB for NoSQL to build AI applications that perform semantic similarity queries.
After completing this module, you'll be able to:
- Store and retrieve vector embeddings in Azure Cosmos DB containers with properly configured vector policies
- Execute vector similarity queries using the VectorDistance function to find semantically similar documents
- Combine vector search with metadata filters and full-text search using hybrid queries
- Implement change feed processing to automatically refresh embeddings when source documents change
- Learn how to optimize query performance and reduce costs for Azure Cosmos DB for NoSQL workloads by configuring indexes and selecting consistency levels.
After completing this module, you'll be able to:
- Analyze query patterns and use metrics to identify performance bottlenecks and missing indexes
- Configure range and composite indexes to optimize filter and sort operations for AI retrieval patterns
- Select the appropriate vector index type based on dataset size, accuracy requirements, and performance goals
- Design indexing policies that balance read performance against write costs for your workload profile
- Choose consistency levels that meet application requirements while minimizing RU consumption
