| Troubleshooting | L37-L56 | Diagnosing and fixing Stream Analytics job issues: error codes (config/data/external/internal), input/output connection failures, query/UDF bugs, and using diagrams, metrics, and resource logs to debug. |
| Best Practices | L57-L75 | Best practices for designing, scaling, and optimizing Stream Analytics jobs: query patterns, performance tuning, reliability, geospatial, time/late events, ML, Cosmos DB, SQL, and alerting. |
| Decision Making | L76-L82 | Guidance on choosing Stream Analytics developer tools, migrating projects from Visual Studio to VS Code, and comparing Azure real-time/stream processing services for your scenario. |
| Architecture & Design Patterns | L83-L87 | Architectural patterns and best practices for designing resilient, geo-redundant Azure Stream Analytics solutions, including reference topologies and high-availability job designs. |
| Limits & Quotas | L88-L93 | Configuring and tuning Stream Analytics streaming units and clusters, including how to resize, scale performance, and understand capacity limits and resource quotas. |
| Security | L94-L113 | Securing Stream Analytics jobs: managed identities for inputs/outputs, private endpoints/VNet integration, data protection, credential rotation, and Azure Policy compliance controls. |
| Configuration | L114-L148 | Configuring Stream Analytics jobs: inputs/outputs (SQL, Cosmos DB, Event Hubs, Kafka, Power BI, Delta Lake, etc.), partitioning, autoscale, compatibility, monitoring, alerts, and error policies. |
| Integrations & Coding Patterns | L149-L168 | Patterns for integrating Stream Analytics with Kafka, Azure ML, Functions, Schema Registry, and for writing UDFs/aggregates, parsing formats, and doing ML/anomaly detection. |
| Deployment | L169-L184 | Deploying, starting/stopping, scaling, and moving Stream Analytics jobs and clusters, plus CI/CD automation via ARM/Bicep, GitHub Actions, Azure DevOps, npm/NuGet, and IoT Edge/Stack Hub. |