What is Azure Data Explorer?
Azure Data Explorer is a fully managed, highly scalable analytics service for near real-time insights into log, telemetry, and time-series data. The service can process petabytes of structured, semi-structured, and unstructured data and typically returns query results within seconds. Its underlying Kusto engine also powers services such as Azure Monitor Logs, Application Insights, and Microsoft Fabric Eventhouse.
Core Features
- Real-time ingestion of streaming data via Event Hub, IoT Hub, Kafka connectors, and other pipelines
- KQL (Kusto Query Language) for complex analytics, time-series functions, and pattern detection; T-SQL is also supported
- Automatic indexing and compression of all ingested data
- Native visualization and dashboards directly in the Azure portal
- Integration with Power BI, Grafana, Kibana, and Azure Monitor
Typical Use Cases
- Log analysis for applications and infrastructure with high query concurrency
- IoT telemetry and sensor analysis with high ingestion rates
- Security Information and Event Management (SIEM) and anomaly detection
- Time-series analysis with forecasting, seasonality detection, and geospatial analysis
Benefits
- Low-latency queries on very large data volumes thanks to columnar storage and automatic partitioning
- Cost-effective through usage-based billing by cluster size and data volume
- Native Azure integration, including with Azure Monitor, Event Hub, and Power BI
- Migration path toward Microsoft Fabric Eventhouse for teams standardizing on a unified Fabric analytics portfolio
Integration with innFactory
As a Microsoft Solutions Partner, innFactory supports you with Azure Data Explorer: architecture, data ingestion pipelines, KQL optimization, integration with monitoring systems, and evaluating a migration to Microsoft Fabric Eventhouse.
Typical Use Cases
Frequently Asked Questions
What is Azure Data Explorer?
Azure Data Explorer (also called Kusto) is a fully managed analytics service for near real-time analysis of large data volumes, particularly logs, telemetry, and time-series data. It uses a relational model with strongly typed tables and typically returns query results on petabyte-scale data within milliseconds to seconds.
What is Azure Data Explorer optimized for?
For interactive analytics on streaming and time-series data such as logs, IoT telemetry, and application metrics. Queries on very large data volumes typically return results within seconds.
What is KQL (Kusto Query Language)?
KQL is Microsoft's query language for Data Explorer, optimized for time-series and log data. It resembles SQL but adds functions for aggregations, time-series analysis, and pattern matching; T-SQL is also supported.
How does Data Explorer relate to Microsoft Fabric Eventhouse?
Eventhouse in Microsoft Fabric Real-Time Intelligence is built on the same Kusto engine as Azure Data Explorer. Microsoft provides migration paths and database shortcuts between the two, allowing existing Azure Data Explorer environments to move to Fabric gradually. Azure Data Explorer remains available as a standalone Azure service.
When should I use Data Explorer instead of Log Analytics?
Data Explorer suits custom applications, custom schemas, and very high data volumes with unrestricted KQL access. Log Analytics (part of Azure Monitor) targets Azure-native monitoring with pre-configured dashboards and alerts and uses the same underlying query language.
Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of Azure (official documentation). This page does not represent an offer by Azure.
