KQL Series – high level of data ingestion- setup

In this blog post let us stop for a second and see where we are in this whole create an Azure Data Explorer cluster and ingest data.
High level summary below:

What is Azure Data Explorer:

It is a fast and scalable data analytics service that can be used to ingest, store, and analyze large volumes of data from various sources. Here are the steps to ingest data using Azure Data Explorer:

  1. Create a database and a table: The first step is to create a database and a table in Azure Data Explorer where the data will be stored. You can create a database and a table using Azure Portal, Azure PowerShell, or Azure CLI.
  2. Prepare the data for ingestion: Before ingesting the data into Azure Data Explorer, you need to prepare the data. This includes cleaning and formatting the data in a way that is compatible with Azure Data Explorer.
  3. Choose a data ingestion method: Azure Data Explorer supports several data ingestion methods, including Azure Data Factory, Azure Stream Analytics, Event Hubs, and more. Choose the method that best suits your needs.
  4. Ingest the data: Once you have chosen the data ingestion method, you can start ingesting the data into Azure Data Explorer. The data will be automatically indexed and stored in the table you created in step 1.
  5. Verify the data ingestion: After the data is ingested, you should verify that it was successfully ingested and is available for analysis. You can use Kusto Query Language (KQL) to query the data and perform analytics.

In summary, to ingest data using Azure Data Explorer, we need to create a database and a table, prepare the data, choose a data ingestion method, ingest the data, and verify the data ingestion.


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