Blog Post

KQL Series – ingesting data using Azure Stream Analytics


This blog post is about another method of ingesting data into Azure Data Explorer.

Azure Stream Analytics is a cloud-based stream processing service that allows us to ingest and process real-time data from various sources. We can use Azure Stream Analytics to ingest data into Azure Data Explorer in real-time. Here’s how:

  1. Create an Azure Data Explorer Table: The first step is to create a table in Azure Data Explorer that will receive the real-time data.
  2. Create an Azure Stream Analytics Job: The next step is to create an Azure Stream Analytics job that will ingest the data and send it to the Azure Data Explorer table. You will need to specify the input source of the real-time data and the output destination of the data in Azure Data Explorer.
  3. Define a Query: In the Azure Stream Analytics job, you will need to define a query that transforms the real-time data and sends it to Azure Data Explorer.
  4. Start the Azure Stream Analytics Job: Once you have defined the query, you can start the Azure Stream Analytics job. The job will ingest the real-time data and send it to Azure Data Explorer.

Azure Stream Analytics provides us a very user-friendly interface that allows us to monitor the job and troubleshoot any issues.

This has to be one of the easiest ways (outside of ingesting csv) to get data into Azure Data Explorer for us to play around with.


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