Azure Stream Analytics is generally available across Azure ecosystem – global Azure, Azure IoT Edge and Azure Stack Hub. It allows developers to build architectures for stream processing in different scenarios using the same tools and query language. This blog will provide common scenarios recommendation to help to make the best choice for deliver near-real-time analytical intelligence for your organization.
Azure Stream Analytics on Azure provides large-scale analytics in the cloud. It is designed to analyze and process high volumes of fast streaming data from multiple sources simultaneously. Patterns and relationships can be identified in information extracted from several input sources including devices, sensors, clickstreams, social media feeds, and applications. These patterns can be used to trigger actions and initiate workflows such as creating alerts, feeding information to a reporting tool, or storing transformed data for later use.
The following scenarios are examples of when you can use Azure Stream Analytics:
Real-time analytics on Point of Sale for inventory control and anomaly detection.
Remote monitoring and predictive maintenance of high value assets.
For more details about Stream Analytics on cloud, please see here.
Azure IoT Edge
Azure Stream Analytics on IoT Edge extends the streaming capabilities and analytics from the cloud to the device level. It is designed for scenarios where customers need low latency command control, have limited cloud connectivity or bandwidth, or require regulatory compliance. An Edge job is created in the Azure portal and then deployed as an IoT Edge module without additional code. By processing telemetry streams at the edge, customers can reduce the amount of uploaded data and reduce the time it takes to react to actionable insights.
The following scenarios are examples of when you can use Azure Stream Analytics on IoT Edge:
Local real-time analytics and decision making for vessel managements.
Analyze and anonymize telemetry streams before sending it to the cloud.
Azure Stream Analytics is built into Azure SQL Edge to provide capabilities to stream, process, and analyze relational and non-relational such as JSON, graph and time-series data. It uses the same constructs and capabilities as Azure Stream Analytics on IoT Edge. Customers can pull and run the container image without interacting with the cloud, which allows customers to set up Stream Analytics and stay fully disconnected to the Internet.
Azure Stream Analytics is a hybrid service on Azure Stack Hub. It is an IoT Edge module which is configured in Azure but can be run on Azure Stack Hub. Internet connection is needed when creating ASA Edge job and deploying the module. It provides customers with the ability to build truly hybrid architecture for stream processing in their own private and autonomous cloud.
The following scenarios are examples of when you can use Azure Stream Analytics on Azure Stack Hub:
Real-time analytics on large amounts of confidential data in a facility with top level security.
Process stream data from financial reporting on-premises to meet regulatory requirements.