azure-kusto

To run KQL queries on Azure Data Explorer, you can direct the AI to execute queries across your datasets to extract insights from logs and telemetry. This allows you to perform fast and scalable analysis on large-scale time series data.

Discovering table schemas and data types in Azure Data Explorer is done through schema exploration features. You can identify table structures and columns to understand your data models, which streamlines the exploration of complex telemetry datasets.

Yes, you can list available clusters and databases within your Azure subscription. This resource management capability allows you to view and navigate your Azure Data Explorer environment before executing queries.

Yes, this approach supports aggregations and anomaly detection for telemetry data. You can perform trend analysis and operational monitoring on large-scale logs within Azure Data Explorer to identify meaningful insights.

You need Azure Data Explorer when performing fast and scalable queries on large-scale log, telemetry, and time series data. It is designed to streamline complex data exploration workflows for operational insights where other analytics tools may lack sufficient scalability.