Intelligent Resource Optimization: Mastering Azure Cosmos DB for Enterprise Workloads
summary
A concrete recipe for cutting Azure Cosmos DB costs and latency: claims a 35% cost reduction by combining container consolidation, targeted indexing, TTL for transient data, and autoscale RUs; demonstrates change feed pipelines that use Azure Functions to compute and store feature vectors in a dedicated feature-store container; shows multi-write region setups with custom merge handlers for embedding conflicts; and wires telemetry through Azure Monitor/Application Insights, Azure Data Explorer and Power BI as custom monitoring dashboards to alert on RU throttling and pipeline latency.
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chapters (8)
- 0:00Introduction and agenda
- 0:27Cosmos DB in AI architecture
- 4:11Advanced optimization
- 7:41Technical demonstration
- 10:41Custom monitoring
- 13:40Multi-region architecture
- 15:17Cost optimization strategies
- 17:35Conclusion
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