- Azure Stream Analytics is a service for complex event processing and analysis of streaming data.
Stream Analytics is used to:
- Ingest data from an input, such as an Azure event hub, Azure IoT Hub, or Azure Storage blob container.
- Process the data by using a query to select, project, and aggregate data values.
Write the results to an output, such as Azure Data Lake Storage Gen2, Azure SQL Database, Azure Cosmos DB, Azure Functions, Azure Event Hubs, Microsoft Power BI, or others.
data stream consists of a perpetual series of data, typically related to specific point-in-time event eg environmental measurements recorded by an internet-connected weather sensor
Characteristics of stream processing solutions
Stream processing solutions typically exhibit the following characteristics:
- The source data stream is unbounded - data is added to the stream perpetually.
- Each data record in the stream includes temporal (time-based) data indicating when the event to which the record relates occurred (or was recorded).
- Aggregation of streaming data is performed over temporal windows - for example, recording the number of social media posts per minute or the average rainfall per hour.
- The results of streaming data processing can be used to support real-time (or near real-time) automation or visualization, or persisted in an analytical store to be combined with other data for historical analysis. Many solutions combine these approaches to support both real-time and historical analytics.
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