Overview

Coralogix Contextual Data Analysis extends traditional observability by incorporating “small data” – such as cloud status pages, CI/CD events, Git commits, chat threads, or support tickets – into a unified data pipeline. This approach correlates contextual signals directly with logs, metrics, and traces to offer powerful insights into system behavior. The result is faster troubleshooting, clearer root cause analysis, and reduced noise from false alerts. By enriching existing observability stacks with external context, organizations can achieve deeper visibility and more confident decision-making across dynamic, complex environments.

Features and Capabilities

• Contextual data ingestion – Pulls in signals like cloud provider status pages, CI/CD events, Git commits, CDN logs, Slack threads, support tickets, Intercom chats, and AWS SNS events.
• Intelligent alerting powered by machine learning – Supports dynamic thresholds, new-value detection, cardinality analysis, ratio queries, and sequence queries to reduce false positives.
• Context-driven incident investigation – Automatically correlates logs, traces, and metrics with relevant contextual events for quicker resolution.
• Release benchmarking and feedback loops – Automatically benchmarks each release and maps system performance to deployment events.
• Custom dashboards and queries – Enables advanced visualizations using drag-and-drop UI, Lucene and PromQL support, and multi-level groupings.
• 350+ integrations and REST API access – Broad compatibility with third-party tools and easy extension through API calls.
• Enterprise-grade scalability and security – Built to support large organizations with robust infrastructure and compliance-ready design.