From azure
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. Use when building Stream Analytics jobs with Event Hubs/Kafka, Cosmos DB/SQL outputs, ML/Functions, or IoT Edge, and other Azure Stream Analytics related development tasks. Not for Azure Data Factory (use azure-data-factory), Azure Synapse Analytics (use azure-synapse-analytics), Azure Event Hubs (use azure-event-hubs), Azure Data Explorer (use azure-data-explorer).
npx claudepluginhub atc-net/atc-agentic-toolkit --plugin azureThis skill uses the workspace's default tool permissions.
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.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Builds production-ready Apache Airflow DAGs with patterns for operators, sensors, testing, and deployment. For data pipelines, workflow orchestration, and batch jobs.
Share bugs, ideas, or general feedback.
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: This file may be large. Use the Category Index below to locate relevant sections, then use
read_filewith specific line ranges (e.g.,L136-L144) to read the sections needed for the user's question This skill requires network access to fetch documentation content. Usemcp_microsoftdocs:microsoft_docs_fetchto retrieve full articles.
WebFetch tool if the Microsoft Learn MCP server is not available.| Category | Lines | Description |
|---|---|---|
| Troubleshooting | L31-L49 | 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 | L51-L68 | Guidance on designing, scaling, optimizing, and troubleshooting Stream Analytics jobs, including queries, partitions, time handling, inputs/outputs, metrics, alerts, and geospatial/ML patterns |
| Decision Making | L70-L75 | 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 | L77-L81 | Architectural patterns and best practices for designing resilient, geo-redundant Azure Stream Analytics solutions, including reference topologies and high-availability job designs. |
| Limits & Quotas | L83-L87 | Configuring and tuning Stream Analytics streaming units and clusters, including how to resize, scale performance, and understand capacity limits and resource quotas. |
| Security | L89-L107 | Securing Stream Analytics jobs: managed identities for inputs/outputs, private endpoints/VNet integration, data protection, credential rotation, and Azure Policy compliance controls. |
| Configuration | L109-L142 | 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 | L144-L162 | 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 | L164-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. |
| Topic | URL |
|---|---|
| Select developer tools for Azure Stream Analytics jobs | https://learn.microsoft.com/en-us/azure/stream-analytics/feature-comparison |
| Migrate Stream Analytics projects from Visual Studio to VS Code | https://learn.microsoft.com/en-us/azure/stream-analytics/migrate-to-vscode |
| Choose Azure real-time and stream processing services | https://learn.microsoft.com/en-us/azure/stream-analytics/streaming-technologies |
| Topic | URL |
|---|---|
| Design geo-redundant Azure Stream Analytics job architectures | https://learn.microsoft.com/en-us/azure/stream-analytics/geo-redundancy |
| Apply Azure Stream Analytics solution architecture patterns | https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-solution-patterns |
| Topic | URL |
|---|---|
| Resize Azure Stream Analytics clusters by streaming units | https://learn.microsoft.com/en-us/azure/stream-analytics/scale-cluster |
| Understand and tune Azure Stream Analytics streaming units | https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption |