name: azure-stream-analytics description: 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 ASA jobs with Event Hubs/Kafka, Cosmos DB/SQL/ADX outputs, ML/AML, IoT Edge, or Power BI, 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 Databricks (use azure-databricks), Azure Event Hubs (use azure-event-hubs). compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation. metadata: generated_at: "2026-06-14" generator: "docs2skills/1.0.0"
Azure Stream Analytics Skill
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.
How to Use This Skill
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:
- Preferred: Use
mcp_microsoftdocs:microsoft_docs_fetchwith query stringfrom=learn-agent-skill. Returns Markdown. - Fallback: Use
fetch_webpagewith query stringfrom=learn-agent-skill&accept=text/markdown. Returns Markdown.
Category Index
| Category | Lines | Description |
|---|---|---|
| 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-L74 | 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 | L75-L82 | Guidance on choosing tools, migration paths, autoscaling options, and comparing Azure real-time/stream processing services for designing Stream Analytics solutions. |
| 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-L115 | 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 | L116-L149 | Configuring Stream Analytics jobs: inputs, outputs (SQL, Cosmos DB, Event Hubs, Kafka, Power BI, Delta Lake, etc.), autoscale, ordering, error handling, monitoring, and compatibility settings. |
| Integrations & Coding Patterns | L150-L169 | 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 | L170-L182 | 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. |
Troubleshooting
Best Practices
Decision Making
| 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 and configure autoscale for Stream Analytics SUs | https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-autoscale |
| Choose Azure real-time and stream processing services | https://learn.microsoft.com/en-us/azure/stream-analytics/streaming-technologies |
Architecture & Design Patterns
| Topic | URL |
|---|---|
| Design geo-redundant Azure Stream Analytics job architectures | https://learn.microsoft.com/en-us/azure/stream-analytics/geo-redundancy |
| Scale Azure Stream Analytics jobs with SUs and partitioning | https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-scale-jobs |
Limits & Quotas
| Topic | URL |
|---|---|
| Resize Azure Stream Analytics clusters by streaming units | https://learn.microsoft.com/en-us/azure/stream-analytics/scale-cluster |
| Understand Azure Stream Analytics on IoT Edge limits and support | https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-edge |
| Understand and tune Azure Stream Analytics streaming units | https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption |