CONTEXTUAL DATA ANALYSIS

Correlate Contextual Data with System Health Metrics

Get added context and deeper system observability by ingesting small pockets of data that can be correlated to errors and system health metrics.

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Small Data, Big Value

Sources of small data within an organization are often overlooked as either not holding much value or as noise. However, when we consider diagnostics and the information that we need for system observability, these small pockets of data can hold tremendous value.

Holistic Data Collection

Ingest data from non-traditional data sources for a single, centralized platform to manage, monitor, and alert on system behavior and health.

As data is ingested, Coralogix employs machine learning to correlate data entries in real time for deeper insights and faster troubleshooting.

ML-Powered, Contextual Alerting

The most extensive alerting mechanism in the market, Coralogix pinpoints critical issues and dramatically reduces false positives to avoid alert fatigue. Track the critical parts of your system and SLOs using advanced alerts including:

Dynamic thresholds
‘Ratio between’ queries
New value detection
Cardinality alerts
Query sequences

Built for Fast-Paced Environments

Contextual data around deployments or system changes helps to correlate events with new releases to reduce issue resolution time, decrease maintenance costs, and improve customer satisfaction.

Version benchmarks are generated when new code is released and can be integrated directly to any deployment pipeline to enable teams to implement better feedback loops and keep up with fast-paced development cycles.

Seamless Visualization and Familiar Syntax

Visualize your data anywhere including in our purpose-built UI, Kibana, Grafana, SQL clients, Tableau, or using our CLI and full API support. Our support team will be available to help you migrate your existing Kibana visualizations and alerts or to set up new ones.

Use any syntax, including the familiar ELK syntax, for quick and easy on-boarding and no vendor lock-in.

“On the first day, and without any customization whatsoever, we already received new insights that we were not able to see before. A week in, and our Ops teams across the board were already able to get so much more out of our logs.”