Connect Telemetry Sources for Causal Analysis
Causely requires traces to discover service dependencies and perform effective causal analysis. Without traces, the platform's ability to provide value is severely limited. Start with OpenTelemetry sources to ensure you have comprehensive trace coverage.
To ensure that Causely can reason over a wide range of symptoms and causes, that explain those symptoms, in your environment, it is recommended that you configure additional data sources if they are available.
UI-based configuration
You can configure telemetry sources from the UI. Navigate to https://portal.causely.app/integrations to set up the integrations you need. You can then select the Mediator to which the configuration will be pushed down.
Missing a telemetry source? On that same page, click on + Request a new integration to file a request to add the integration you need.
OpenTelemetry Sources
Start here for essential trace coverage
OpenTelemetry
Enable automatic discovery of service dependencies across both synchronous and asynchronous communications.
Datadog
Use Datadog traces and monitors as trusted signals for causal analysis.
eBPF
Capture kernel-level dependencies and performance signals without modifying code.
Grafana
Use Grafana to bring in metrics, logs, and traces as rich inputs for causal analysis.
Groundcover
Combine high-fidelity eBPF data with Causely’s analytics.
Odigos
Automatically instrument services with OpenTelemetry and feed traces into Causely’s engine.
Application Instrumentation
By leveraging OpenTelemetry and Prometheus, Causely can provide comprehensive observability and causal reasoning for applications written in any language. Among other languages, Causely supports the following languages:
C++
Capture detailed performance traces from high-performance systems to improve causal inference precision.
.NET
Enable cross-service traceability for enterprise and cloud applications.
Erlang/Elixir
Map message-passing and concurrent behavior to detect issues in distributed environments.
Go
Add low-overhead tracing to cloud native applications for faster dependency discovery.
Java
Connect JVM traces and metrics for proactive detection of performance regressions.
JavaScript
Instrument JavaScript-based services to capture backend traces and uncover issues in distributed architectures.
PHP
Include PHP service traces to expose web transaction bottlenecks in causal analysis.
Python
Link Python app traces and metrics to detect slow APIs or resource contention.
Ruby
Enable full-trace visibility across Ruby web and background jobs for causal inference.
Rust
Feed low-level telemetry into Causely to uncover performance and memory-related causes.
Swift
Connect Swift app traces to isolate problems across distributed components.
Infrastructure & Platform
Broad infrastructure and service insights
AWS Managed Services
Integrate CloudWatch metrics and events to infer causes in managed AWS workloads.
Azure Managed Services
Surface reliability issues in Azure services with native telemetry inputs.
GCP Managed Services
Integrate GCP service metrics to infer causes in managed workloads.
Dynatrace
Use Dynatrace traces and metrics to strengthen causal inference and reliability insights.
Instana
Integrate Instana traces and metrics to enhance causal analysis and deepen reliability insights.
Kubernetes
Map pod-level health and resource metrics to their upstream service dependencies.
Prometheus
Use Prometheus metrics and alerts as key signals for detecting and diagnosing service-level issues.
Specialized Sources
Detailed insights for specific technologies
Prometheus Alertmanager
Use Alertmanager alerts as trusted inputs for causal analysis.
Confluent Cloud
Detect message queue congestion and downstream impact from Kafka telemetry.
Checkly
Use API and synthetic check failures as trusted inputs for causal analysis.
Elasticsearch
Integrate Elasticsearch metrics and logs to detect and resolve performance issues.
Istio
Map service mesh communication patterns for precise dependency-aware causal inference.
incident.io
Leverage incident.io alerts as trusted inputs for causal analysis.
MySQL
Identify database query latency or connection errors as causes in distributed issues.
Nobl9
Align SLO breaches with Causely’s causal insights to prioritize reliability work.
PostgreSQL
Link PostgreSQL performance and query issues to affected services in real time.
Snowflake
Surface warehouse congestion or query slowdowns as upstream causes in analytics pipelines.