| Troubleshooting | L37-L56 | Diagnosing and fixing Stream Analytics job issues: error codes (config, data, internal/external), input/output and query problems, and debugging with job diagrams, metrics, logs, and UDF tools. |
| Best Practices | L57-L73 | Best practices for Stream Analytics job design, query patterns, performance tuning, scaling, reliability, time handling, geospatial logic, ML/Cosmos/SQL outputs, and alerting. |
| Decision Making | L74-L81 | Guidance on choosing tools, migration paths, autoscaling options, and comparing Azure real-time/stream processing services for designing Stream Analytics solutions. |
| Architecture & Design Patterns | L82-L87 | Designing resilient, geo-redundant Stream Analytics topologies and scaling jobs using Streaming Units, input/output partitioning, and performance tuning patterns. |
| Limits & Quotas | L88-L94 | Info on Stream Analytics capacity limits, streaming units (SUs), how to size/resize clusters, performance tuning, and specific constraints for Azure Stream Analytics on IoT Edge. |
| Security | L95-L114 | Securing Stream Analytics jobs with managed identities, private endpoints, VNets, data protection, credential rotation, and Azure Policy for outputs like Event Hubs, SQL, ADX, Cosmos DB, and Power BI |
| Configuration | L115-L148 | Configuring Stream Analytics jobs: inputs, outputs (SQL, Cosmos DB, Event Hubs, Kafka, Power BI, Delta Lake, etc.), autoscale, ordering, error handling, monitoring, and compatibility. |
| Integrations & Coding Patterns | L149-L166 | Patterns for integrating Stream Analytics with Kafka, Event Hubs, ML/AML, schema registry, and custom code (C#/JS UDFs/aggregates), plus JSON/Avro parsing and advanced scenarios like HFT. |
| Deployment | L167-L179 | 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. |