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Azure Data Explorer is the go-to tool for handling and querying large volumes of streaming data. This can be ingested from external devices or from internal resource telemetry, such as event logs. It has the ability to scale serverless compute and return query results with ease, with data manipulated using the Kusto Query Language (KQL). KQL supports rich data exploration with time series results returned using minimal amounts of code, compared to SQL equivalent functionality, especially when windowing. This syntax can be written directly into the browser based studio environment for the resource, or coded locally in Azure Data Studio, with an active connection to the cloud compute.
The logical database instance included in the resource allows the creation of tables, views and functions in a hierarchy of objects similar to an SQL Server instance.
To resource supports inputs from Azure Event Hub/IoT Hub and outputs to Power BI dashboards, amongst others. Table entities then use mapping expressions to allow for a continuous flow (stream) of data through objects as its refined during the analytical process. Given this, it is often important to configure data retention policies on objects so only important, hot data is retained.
See MS Learn for more information on this Resource here.
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