Connect Telemetry Sources for Causal Analysis

Traces Are Essential

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.

tip

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.

Mimir

Use Mimir metrics and alerts as key signals for detecting and diagnosing service-level issues.

Cortex

Use Cortex 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.