developmentContext + Dynatrace
Transform Dynatrace software intelligence into searchable operational knowledge with an enterprise-grade knowledge graph
OVERVIEW
Dynatrace is the software intelligence platform where engineering organizations gain full-stack observability across applications, infrastructure, and digital experience. Dynatrace's AI-powered problem detection, automatic topology discovery, and root cause analysis generate a wealth of operational intelligence: how services depend on each other, which code-level changes correlate with performance degradation, how infrastructure components affect application behavior, and what user experience patterns indicate emerging issues. But this intelligence is confined within Dynatrace's interface, disconnected from the Jira tickets tracking performance improvements, the GitHub pull requests introducing code changes, the Confluence pages documenting architecture decisions, and the Slack channels where teams coordinate incident response.
Context connects to your Dynatrace environment and extracts the organizational knowledge embedded in problem records, Smartscape topology data, management zone configurations, SLO definitions, synthetic monitor results, and Davis AI findings. Using permission-aware indexing that respects your Dynatrace management zone permissions and group-based access controls, Context builds a knowledge graph that maps relationships between services, infrastructure components, problems, teams, and the broader context from your entire tool stack.
Unlike cloud-based search tools that require your observability data to be processed on external infrastructure, Context deploys entirely on your network -- on-premise, in your VPC, or in air-gapped environments. Your Dynatrace topology data, problem detection records, and performance intelligence never leave your control. For defense contractors monitoring classified applications, aerospace companies managing mission-critical software systems, and financial institutions overseeing high-frequency trading platforms, application performance data reveals system architecture, capacity thresholds, and failure patterns. Context ensures this intelligence remains within your security boundary while making it searchable and actionable. Every answer is backed by citations to specific Dynatrace problems, entities, or SLO configurations, maintaining full traceability.
KEY CAPABILITIES
Key Capabilities
- 01Permission-aware indexing of Dynatrace problems, entities, management zones, SLOs, and synthetic monitors that respects management zone permissions and group-based access controls
- 02Smartscape topology knowledge extraction that makes Dynatrace's automatically discovered service dependencies and infrastructure relationships searchable across your entire tool stack
- 03Problem pattern analysis that indexes Davis AI root cause findings and correlates them with code changes in GitHub, deployment events in Jenkins, and incident records in PagerDuty
- 04SLO context indexing that connects Dynatrace service level objectives to the business requirements documented in Jira and the architectural decisions recorded in Confluence
- 05Cross-tool performance correlation that links Dynatrace performance anomalies to GitHub deployments, feature flag changes, and Jira release tickets automatically
- 06Digital experience knowledge mapping that connects real user monitoring and synthetic test results to the product decisions and feature rollouts tracked across your project management tools
USE CASES
Use Cases
Deployment Impact Analysis and Performance Governance
After every deployment, teams need to understand whether the change affected application performance or user experience. Context connects Dynatrace problem detections and performance baselines to the GitHub pull requests that were deployed, the Jenkins pipeline that executed the deployment, and the Jira ticket tracking the feature. Engineers can ask "did the last deployment to the checkout service cause any performance regressions?" and get citation-backed answers linking Dynatrace anomaly detection to the specific code changes and deployment records.
Service Dependency Discovery and Architecture Documentation
Dynatrace's Smartscape topology automatically discovers how services, processes, and hosts relate to each other, but this topology data is not connected to the architectural documentation in Confluence or the service ownership in Jira. Context builds a knowledge graph that links Dynatrace's discovered topology to documented architecture, team ownership, and business context. Architects can ask "what services depend on the payment gateway and who owns them?" and receive answers spanning Dynatrace topology data, Jira project ownership, and Confluence architecture documents.
Problem Resolution Knowledge Reuse
When Dynatrace's Davis AI detects a problem, responders need context from previous similar events: what was the root cause, how was it resolved, and what follow-up actions were taken? Context indexes Dynatrace problem records alongside PagerDuty incident timelines, Jira remediation tickets, and Confluence post-mortem documents. When a similar performance degradation occurs, the full resolution playbook from previous incidents is instantly available with citations to every source.
Capacity Planning and Infrastructure Optimization
Infrastructure teams need to correlate Dynatrace resource utilization data with business growth trends and architectural changes. Context connects Dynatrace infrastructure metrics context to Jira capacity planning epics, Confluence scaling guidelines, and the Terraform configurations defining infrastructure provisioning. Teams can ask "what is the current resource utilization trend for the recommendation engine cluster and what scaling thresholds have we defined?" and get connected answers spanning observability data, planning documents, and infrastructure code.
HOW IT WORKS
How It Works
DATA FLOW
SECURITY & COMPLIANCE
Security & Compliance
SOC 2 Type II
GDPR
HIPAA
ISO 27001DEPLOYMENT
Deployment Options
DEPLOYMENT ARCHITECTURE
FREQUENTLY ASKED QUESTIONS
Frequently Asked Questions
How does Context connect to Dynatrace?
Context integrates with Dynatrace through the platform's REST API (v2) and Environment API using a dedicated API token with read-only scopes. Once configured, Context indexes problems, monitored entities, management zone configurations, SLO definitions, and synthetic monitor results. The connection is read-only -- Context never modifies your Dynatrace environment, alerting profiles, or management zones. All indexing and processing happens on your infrastructure, whether deployed on-premise, in your VPC, or in an air-gapped environment.
Does Context index raw Dynatrace metrics or trace data?
No. Context indexes the knowledge artifacts in Dynatrace -- problem records, entity metadata, topology relationships, SLO definitions, and Davis AI findings -- rather than raw metrics, traces, or log streams. This approach captures the operational intelligence your engineering team and Dynatrace's AI have produced, making performance insights and problem resolution patterns searchable. Raw observability data remains in Dynatrace and is referenced through citations when relevant.
Can Context work with Dynatrace in regulated environments?
Yes. Context deploys entirely on your infrastructure with no external data processing dependencies. For organizations operating under FedRAMP, CMMC, SOC 2, or PCI DSS requirements, Context ensures that Dynatrace topology data, problem detection records, and performance intelligence never leave your controlled environment. The on-premise deployment model keeps sensitive observability intelligence within your security boundary.
How does Context handle Dynatrace management zone permissions?
Context respects Dynatrace's management zone-based access model. When users search through Context, they only see results from entities and problems within management zones they have access to in Dynatrace. Problem details, entity metadata, and SLO configurations are surfaced only to users with appropriate Dynatrace permissions, ensuring that sensitive performance data for restricted environments is not exposed to unauthorized personnel.
Can Context leverage Dynatrace's Davis AI findings?
Yes. Context indexes Davis AI problem detection results, including root cause analysis, impact assessment, and correlated events. These AI-generated findings are linked into the broader knowledge graph alongside related content from GitHub, PagerDuty, Jira, and Confluence. This means that when Davis AI identifies a root cause, Context can connect that finding to the specific code change, deployment event, and team responsible, providing deeper context than either tool alone.
Which Dynatrace deployment models does Context support?
Context works with Dynatrace SaaS, Managed, and ActiveGate deployment models. The integration connects through Dynatrace's standard API endpoints. For Managed deployments in highly regulated environments, Context's on-premise deployment complements Dynatrace's self-hosted model, ensuring that no observability data leaves your infrastructure at any point in the pipeline.
SETUP OVERVIEW
Setup Overview
Connecting Dynatrace to Context requires Dynatrace administrator access and typically takes around 15 minutes. The process involves creating a dedicated API token with read-only scopes for problems, entities, SLOs, and topology data, configuring which management zones to index, and mapping Dynatrace groups to Context access controls. Context handles the rest -- indexing begins automatically and the knowledge graph starts building within minutes. No changes to your Dynatrace environment configuration or monitoring setup are required.
RELATED INTEGRATIONS
Related Integrations
Datadog
Connect Context to Datadog to extract operational knowledge from monitors, dashboards, and incident investigations. On-premise deployment with permission-aware indexing.
PagerDuty
Connect Context to PagerDuty to extract incident response knowledge from alerts, escalation policies, and post-incident reviews. On-premise deployment with permission-aware indexing.
GitHub
Connect GitHub to Context and transform code reviews, issues, and pull requests into searchable enterprise knowledge. Works with GitHub Enterprise Cloud and GitHub Enterprise Server.
Jira
Connect Context to Jira to transform tickets, epics, and project history into connected organizational knowledge. Permission-aware indexing with on-premise deployment.
Grafana
Connect Context to Grafana to extract operational knowledge from dashboards, alerts, and annotations. On-premise deployment with permission-aware indexing.
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