| Troubleshooting |
L37-L58 |
Diagnosing and fixing Stream Analytics job errors (config, data, internal/external), performance issues, and input/output/query problems using logs and job diagrams (logical/physical). |
| Best Practices |
L59-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-L82 |
Guidance on choosing Stream Analytics tools and services, migrating from Visual Studio/.NET UDFs, and configuring autoscale and real-time processing options. |
| Architecture & Design Patterns |
L83-L88 |
Designing resilient, geo-redundant Stream Analytics topologies and scaling jobs using Streaming Units, input/output partitioning, and performance tuning patterns. |
| Limits & Quotas |
L89-L95 |
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 |
L96-L116 |
Securing Stream Analytics jobs: managed identities for inputs/outputs (Event Hubs, SQL, Synapse, Cosmos, Blob, Service Bus, Power BI), VNet/private endpoints, data protection, and Azure Policy compliance. |
| Configuration |
L117-L149 |
Configuring Stream Analytics jobs: inputs, outputs (Cosmos DB, SQL, Event Hubs, Kafka, Power BI, Delta Lake, etc.), autoscale, ordering, error handling, monitoring, and compatibility. |
| Integrations & Coding Patterns |
L150-L166 |
Patterns for connecting Stream Analytics to Kafka/SQL/ML, using UDFs/aggregates, handling complex formats, HFT scenarios, and managing jobs via .NET and schema registry. |
| Deployment |
L167-L180 |
Automating deployment, migration, and lifecycle (start/stop/delete) of Stream Analytics jobs and clusters using ARM/Bicep, GitHub Actions, Azure DevOps, REST, and edge/Stack Hub tools |