azure-kusto

Query Azure Data Explorer using KQL by connecting to your ADX cluster and running queries to retrieve logs, telemetry, and time-series data for rapid insight generation. You can apply aggregations and anomaly detection for dashboards.

Yes, you can use KQL to analyze IoT telemetry and time-series data in ADX. The skill applies KQL queries to retrieve and aggregate telemetry, perform anomaly detection, and monitor performance across clusters and databases.

Yes, you need knowledge of KQL syntax to retrieve telemetry from Azure Data Explorer. The skill requires understanding KQL to query clusters, enumerate databases, and discover table schemas for effective data analysis.

Discover table schemas and metadata in an ADX database by querying the cluster with KQL. This allows you to understand data models, list available clusters and databases, and manage resources for monitoring.

The best way to perform security analytics on massive log datasets in ADX is using KQL queries. This approach provides fast, scalable querying to retrieve logs, apply aggregations, and detect anomalies for security reports.

Yes, this skill works with performance monitoring and anomaly detection across ADX clusters. It uses KQL to analyze telemetry and time-series data, enabling you to generate insights and monitor resources across databases.