New in Kloudfuse 3.0
K-Lens uses outlier detection to analyze thousands of attributes within high-dimensional data, offering heatmaps and multi-attribute charts that quickly pinpoint the sources of specific issues.
Real-time cardinality analysis detects high-volume data during ingestion, lowering storage and processing costs while enhancing manageability.
Metrics roll-ups enhance query performance during diagnostics and troubleshooting.
Kloudfuse offers comprehensive analytics, reporting, and dashboards across all observability streams, including metrics, events, logs, and traces. Powered by Apache Pinot, our real-time OLAP datastore ensures ultra-low-latency analytics at high throughput, designed for high volume observability data.
Visualize service dependencies and application relationships with interactive service maps and topology views. Investigate incidents in real time with drill-down dashboards or query using languages like PromQL, LogQL, TraceQL, GraphQL, and SQL. Kloudfuse also supports both embedded and external Grafana dashboards, providing users with flexible access and customization options.
Utilize our advanced anomaly and outlier detection methods, including rolling quantile, SARIMA, DBScan, and seasonal decomposition. For root cause analysis, we apply the Pearson Correlation Coefficient to correlate metrics, events, logs, and traces (MELT) data.
New in Kloudfuse 3.0: Introducing Prophet
With Prophet, Kloudfuse manages irregular time series that include missing values and outliers, such as gaps from outages or low activity. This results in less tuning and improved forecast, even with limited training data.
Kloudfuse’s patent-pending fingerprinting technology automatically extracts patterns from log messages during ingestion. This helps in analyzing logs by severity, source, version, and other parameters, aiding root cause analysis and detecting unexpected log signatures as anomalies for further investigation.
