Basic Troubleshooting

  1. What if the secure connection test fails during input setup?
    Delete the input and attempt to add it again.
  2. Unable to Receive Data from the Input Stream? Error Indicates Deserialization Problem.
    Potential Causes:
    • Malformed events: The incoming data may have issues or be improperly formatted.
    • Incorrect serialization format configuration for the input source.
      Solutions:
    • Test the incoming data connection. Click on three dots present on right side of your desired input. Click on Test.
    • Check the serialization format in the Output blade under Stream Analytics Job.
  3. Receiving the Message ‘You Haven’t Configured Diagnostic Settings for This Job Yet’ when starting the Job.
    You can either disregard the message or set up diagnostic settings in the dedicated pane.
  4. The ‘Add Output’ button appears to be inactive when attempting to create a new output.
    You can’t add a new output while the job is already running. Go to Overview and then click on Stop to stop the running job.
  5. I can’t find my dataset in PowerBi under the datasets tab.
    • Visit the Raspberry Pi Web Simulator to check if the data is still being sent as input to Azure Portal.
    • Run the query to see if the output stream is working properly.

How to use Azure Stream Analytics for stream processing

Azure Stream Analytics is a managed stream processing engine for real-time data analysis from various sources, like devices and applications. It identifies patterns and triggers actions, making it useful for alerts, reporting, and data storage. It can run on Azure IoT Edge for processing data on IoT devices.

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Where to use Azure Stream Analytics?

Streaming ETL pipeline to Azure Storage in Parquet format. Event driven applications with Azure SQL Database and Azure Cosmos DB. Analyze real-time telemetry streams and logs from applications and IoT devices. Real-time dashboarding with Power BI. Anomaly detection to detect spikes, dips, and slow positive and negative changes in sensor values. Geospatial analytics for fleet management and driverless vehicles. Remote monitoring and predictive maintenance of high value assets. Clickstream analytics to determine customer behavior....

What are the benefits of Azure Stream Analytics?

Ease of Use: Easily connect sources and sinks, ingest data from Event Hubs, IoT Hub, and Blob storage. No-code editor simplifies job development and flexible output routing. Programmer Productivity: Stream Analytics uses SQL with temporal constraints. Create jobs with developer tools, support CEP, and extend via custom functions. Fully Managed: Azure handles infrastructure and updates. Cloud or Edge: Run in the cloud for scale or on IoT Edge/Azure Stack for low-latency processing. Low Total Cost: Pay only for consumed streaming units, no upfront costs. Mission-Critical: Offers reliability, security, compliance, and guarantees exactly-once event processing. Reliability: Ensures exactly-once processing, recovery, and high availability. Security: Encrypts communications, supports TLS 1.2, and can use Azure Virtual Networks. Performance: Processes millions of events with low latencies, scalability via partitioning, powered by Trill engine....

Basic Troubleshooting

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Frequently Asked Questions on How to use Azure Stream Analytics for stream processing

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