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Expert knowledge for Azure Stream Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment.
2 of 24 test cases passed with the skill.
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Lift withheld: 4 comparable cases; this report requires 8.
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This skill provides expert guidance for Azure Stream Analytics. Covers troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.
IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g.,
L35-L120), useread_filewith the specified lines. For categories with file links (e.g.,[security.md](security.md)), useread_fileon the linked reference file
IMPORTANT for Agent: If
metadata.generated_atis more than 3 months old, suggest the user pull the latest version from the repository. Ifmcp_microsoftdocstools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.| Category | Lines | Description |
|---|---|---|
| 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 |
test cases passed with the skill
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