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. Search for sources that provide Traces to ensure you have comprehensive trace coverage.

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.

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.

Telemetry sources are organized into categories based on their purpose and the types of inputs they provide to Causely's causal reasoning engine.

Signal Types

Different telemetry sources provide different types of signals to Causely's causal reasoning engine:

• Infrastructure Entities: Complete infrastructure topology including compute, storage, and networking resources
• Logs: Application and container logs for correlation with root causes
• Metrics: Performance metrics from applications and infrastructure
• Service Discovery: Automatic discovery of services, workloads, and infrastructure components
• SLOs: Service Level Objectives and reliability metrics
• Symptoms: Automatic symptom detection from metrics, traces, and external monitoring systems
• Traces: Distributed traces for service dependency discovery and communication monitoring

Telemetry Sources by Categories

Infrastructure Scraper

Discovers infrastructure resources such as VMs, containers, clusters, databases, load balancers, queues, and disks. It imports their topology, metadata, and health indicators, forming the backbone of Causely's environment-specific model for causal mapping of infrastructure-level failure modes.

Inputs Provided to Causely: Metrics, Infrastructure Entities, Symptoms, sometimes Service Discovery, sometimes Logs

AWS Managed Services

Integrate CloudWatch metrics and events to infer causes in managed AWS workloads.Signals: Infrastructure Entities, Metrics, Service Discovery, Symptoms

Azure Managed Services

Surface reliability issues in Azure services with native telemetry inputs.Signals: Infrastructure Entities, Metrics, Service Discovery, Symptoms

Docker

Map container-level health and resource metrics to their upstream service dependencies.Signals: Infrastructure Entities, Logs, Metrics, Service Discovery, Symptoms

GCP Managed Services

Integrate GCP service metrics to infer causes in managed workloads.Signals: Infrastructure Entities, Logs, Metrics, Service Discovery, Symptoms

Kubernetes

Map pod-level health and resource metrics to their upstream service dependencies.Signals: Infrastructure Entities, Logs, Metrics, Service Discovery, Symptoms

Nomad

Map task-level health and resource metrics to their upstream service dependencies.Signals: Infrastructure Entities, Logs, Metrics, Service Discovery, Symptoms

Logs

Ingests log-derived signals (errors, exceptions, warnings) used for enhanced descriptions and remediation after a root cause has been inferred.

Inputs Provided to Causely: Logs

Grafana

Use Grafana to bring in metrics, logs, and traces as rich inputs for causal analysis.Signals: Logs, Metrics, Traces

Metrics & Symptoms

Provides quantitative telemetry (latency, saturation, throughput, resource usage) and domain-specific metrics (for example, Redis, RabbitMQ). These signals populate Causely's Attribute Dependency Graph, enabling reasoning about performance degradation and failure symptom chains.

Inputs Provided to Causely: Metrics, Symptoms

Prometheus

Use Prometheus metrics and alerts as key signals for detecting and diagnosing service-level issues.Signals: Metrics, Symptoms

Middleware

Connects Causely to critical data systems (for example, databases, caches, search systems, and message queues). These integrations pull topology, performance attributes, and state indicators to reveal bottlenecks and root causes within data infrastructure.

Inputs Provided to Causely: Metrics, Infrastructure Entities, Symptoms

Confluent Cloud

Detect message queue congestion and downstream impact from Kafka telemetry.Signals: Infrastructure Entities, Metrics, Symptoms

Elasticsearch

Integrate Elasticsearch metrics and logs to detect and resolve performance issues.Signals: Infrastructure Entities, Logs, Metrics, Symptoms

MySQL

Identify database query latency or connection errors as causes in distributed issues.Signals: Infrastructure Entities, Metrics, Symptoms

PostgreSQL

Link PostgreSQL performance and query issues to affected services in real time.Signals: Infrastructure Entities, Metrics, Symptoms

Snowflake

Surface warehouse congestion or query slowdowns as upstream causes in analytics pipelines.Signals: Infrastructure Entities, Metrics, Symptoms

Service Communication

Provides traces and metrics through standard OTEL-based data pipelines (eBPF, Collectors, dual-shipping from Datadog, Odigos, groundcover, etc.). These sources enrich Causely's service graph and enable call-level causal reasoning.

Inputs Provided to Causely: Traces, Metrics, Symptoms

C++

Capture detailed performance traces from high-performance systems to improve causal inference precision.Signals: Metrics, Symptoms, Traces

Datadog

Use Datadog traces and monitors as trusted signals for causal analysis.Signals: Metrics, Symptoms, Traces

Dynatrace

Use Dynatrace traces and metrics to strengthen causal inference and reliability insights.Signals: Metrics, Symptoms, Traces

eBPF

Capture kernel-level dependencies and performance signals without modifying code.Signals: Metrics, Symptoms, Traces

Groundcover

Combine high-fidelity eBPF data with Causely's analytics.Signals: Metrics, Symptoms, Traces

Instana

Integrate Instana traces and metrics to enhance causal analysis and deepen reliability insights.Signals: Metrics, Symptoms, Traces

Istio

Map service mesh communication patterns for precise dependency-aware causal inference.Signals: Metrics, Traces

Odigos

Automatically instrument services with OpenTelemetry and feed traces into Causely's engine.Signals: Metrics, Symptoms, Traces

OpenTelemetry

Enable automatic discovery of service dependencies across both synchronous and asynchronous communications.Signals: Metrics, Symptoms, Traces

SLO Providers

Supplies SLO (Service Level Objective) definitions and burn rate information (for example, from Nobl9). Causely uses SLOs as high-priority symptoms and impact indicators in causal reasoning.

Inputs Provided to Causely: SLOs

Nobl9

Align SLO breaches with Causely's causal insights to prioritize reliability work.Signals: SLOs

Symptom Activation

Allows Causely to treat external alert triggers (for example, from Datadog Monitors, Checkly, Alertmanager) as explicit symptoms within the causal graph. This helps Causely analyze "why this alert fired" and resolve noisy or ambiguous alerts.

Inputs Provided to Causely: Symptoms

Prometheus Alertmanager

Use Alertmanager alerts as trusted inputs for causal analysis.Signals: Symptoms

Checkly

Use API and synthetic check failures as trusted inputs for causal analysis.Signals: Symptoms

incident.io

Leverage incident.io alerts as trusted inputs for causal analysis.Signals: Symptoms

Search and Filter Telemetry Sources

To search and filter telemetry sources by name, category, or signal type, go to the Supported Technologies page.