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Dt Obs Gcp

  • 933 installs
  • 119 repo stars
  • Updated July 29, 2026
  • dynatrace/dynatrace-for-ai

dt-obs-gcp is a Dynatrace observability skill that instruments Google Cloud GKE, Cloud Run, and GCP services with dashboards, SLOs, and AI workload telemetry for developers operating production cloud deployments.

About

dt-obs-gcp is a dynatrace-for-ai skill that configures Dynatrace observability across Google Cloud Platform workloads including GKE clusters, Cloud Run services, and native GCP integrations. Developers invoke it when production or staging environments on GCP need unified dashboards, service-level objectives, and AI-specific workload telemetry beyond default Cloud Monitoring. The skill guides Dynatrace agent deployment, GCP service instrumentation, and SLO configuration so engineering teams gain full-stack visibility into containerized and serverless AI pipelines running on Google Cloud.

  • GCP instrumentation
  • Dynatrace setup
  • GKE and Cloud Run
  • SLO dashboards
  • AI workload traces

Dt Obs Gcp by the numbers

  • 933 all-time installs (skills.sh)
  • +68 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #322 of 1,039 Cloud & Infrastructure skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs933
repo stars119
Last updatedJuly 29, 2026
Repositorydynatrace/dynatrace-for-ai

How do you monitor GCP workloads with Dynatrace?

Configure Dynatrace observability on Google Cloud—instrument GKE, Cloud Run, and GCP services with dashboards, SLOs, and AI workload telemetry.

Who is it for?

Platform engineers running AI workloads on Google Cloud who need Dynatrace full-stack observability across GKE, Cloud Run, and GCP services.

Skip if: Skip dt-obs-gcp for non-GCP clouds, local-only development without deployment targets, or teams using only native Cloud Monitoring without Dynatrace.

When should I use this skill?

User needs Dynatrace setup on GCP, GKE or Cloud Run monitoring, SLO configuration, or AI workload telemetry on Google Cloud

What you get

Dynatrace GCP instrumentation, observability dashboards, SLO definitions, and AI workload telemetry configuration

  • Dynatrace GCP instrumentation config
  • observability dashboards
  • SLO definitions

Files

SKILL.mdMarkdownGitHub ↗

GCP Cloud Infrastructure

Monitor and analyze GCP resources using Dynatrace Smartscape and DQL. Query GCP services, manage organizational hierarchy, audit security posture, and track resource ownership across your GCP infrastructure.

When to Use This Skill

Use this skill when the user needs to work with GCP resources in Dynatrace. Load the reference file for the task type:

TaskFile to load
Inventory and topology queries(no additional file — use core patterns above)
Compute Engine instances, machine types, IP addressesLoad references/compute-instances.md
GKE clusters, node pools, pods, deployments, services, RBACLoad references/kubernetes-gke.md
Cloud Run services, revisions, executionsLoad references/serverless-containers.md
VPC networks, subnets, routes, DNS recordsLoad references/networking-dns.md
Pub/Sub topicsLoad references/messaging-pubsub.md
IAM service accounts, roles, Secret ManagerLoad references/iam-security.md
Monitoring dashboards, logging, saved queriesLoad references/monitoring-logging.md
GCP projects, regions, organizational hierarchyLoad references/resource-management.md
Resource ownership, GCP labels, organizational structureLoad references/resource-ownership.md

---

Core Concepts

Entity Types

GCP resources use the GCP_* prefix and can be queried using the smartscapeNodes function. All GCP entities are automatically discovered and modeled in Dynatrace Smartscape.

Compute: GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE, GCP_COMPUTE_GOOGLEAPIS_COM_ADDRESS Networking: GCP_COMPUTE_GOOGLEAPIS_COM_NETWORK, GCP_COMPUTE_GOOGLEAPIS_COM_SUBNETWORK, GCP_COMPUTE_GOOGLEAPIS_COM_ROUTE, GCP_DNS_GOOGLEAPIS_COM_RESOURCERECORDSET Kubernetes (GKE): GCP_K8S_IO_POD, GCP_K8S_IO_NODE, GCP_K8S_IO_SERVICE, GCP_K8S_IO_SERVICEACCOUNT, GCP_K8S_IO_PERSISTENTVOLUMECLAIM, GCP_APPS_K8S_IO_DEPLOYMENT, GCP_APPS_K8S_IO_STATEFULSET, GCP_CONTAINER_GOOGLEAPIS_COM_NODEPOOL, GCP_RBAC_AUTHORIZATION_K8S_IO_CLUSTERROLEBINDING, GCP_RBAC_AUTHORIZATION_K8S_IO_ROLEBINDING Serverless: GCP_RUN_GOOGLEAPIS_COM_SERVICE, GCP_RUN_GOOGLEAPIS_COM_REVISION, GCP_RUN_GOOGLEAPIS_COM_EXECUTION IAM & Security: GCP_IAM_GOOGLEAPIS_COM_SERVICEACCOUNT, GCP_IAM_GOOGLEAPIS_COM_ROLE, GCP_SECRETMANAGER_GOOGLEAPIS_COM_SECRETVERSION Messaging: GCP_PUBSUB_GOOGLEAPIS_COM_TOPIC Monitoring: GCP_MONITORING_GOOGLEAPIS_COM_DASHBOARD, GCP_LOGGING_GOOGLEAPIS_COM_SAVEDQUERY Infrastructure: GCP_REGION

Common GCP Fields

All GCP entities include:

  • gcp.project.id — GCP project identifier
  • gcp.region — GCP region (e.g., us-central1)
  • gcp.zone — GCP zone (e.g., us-central1-a)
  • gcp.organization.id — GCP organization identifier
  • gcp.resource.name — Resource name
  • gcp.resource.type — Resource type identifier
  • gcp.asset.type — GCP asset type
  • gcp.object — JSON blob containing full resource configuration

GCP Organizational Hierarchy

GCP resources are organized in a hierarchy:

  • Organization — Top-level container (gcp.organization.id)
  • Folder — Logical grouping within an organization
  • Project — Resource container (gcp.project.id)
  • Region/Zone — Physical location (gcp.region, gcp.zone)

Entity Naming Convention

GCP entity types follow the pattern GCP_<SERVICE_API>_<RESOURCE>:

  • Service API maps to the Google API domain (e.g., compute.googleapis.comCOMPUTE_GOOGLEAPIS_COM)
  • Resource is the specific resource type (e.g., INSTANCE, NETWORK)

Examples:

  • GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE — Compute Engine VM
  • GCP_K8S_IO_POD — GKE pod
  • GCP_RUN_GOOGLEAPIS_COM_SERVICE — Cloud Run service

---

Query Patterns

All GCP queries build on four core patterns. Master these and adapt them to any entity type.

Pattern 1: Resource Discovery

List resources by type, filter by project/region/zone, summarize counts:

smartscapeNodes "GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE"
| fields name, gcp.project.id, gcp.region, gcp.zone, gcp.resource.name

To list all GCP resource types, replace with "GCP_*" and add | summarize count = count(), by: {type} | sort count desc. Add filters like | filter gcp.project.id == "<PROJECT_ID>" or | filter gcp.region == "<REGION>" to scope results.

Pattern 2: Configuration Parsing

Parse gcp.object JSON for detailed configuration fields:

smartscapeNodes "GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE"
| parse gcp.object, "JSON:gcpjson"
| fieldsAdd machineType = gcpjson[configuration][resource][machineType],
            status = gcpjson[configuration][resource][status]
| fields name, gcp.project.id, machineType, status

GCP configuration fields are nested under gcpjson[configuration][resource][...] for primary resource attributes and gcpjson[configuration][additionalAttributes][...] for extended properties.

Pattern 3: Relationship Traversal

Follow relationships between resources:

smartscapeNodes "GCP_COMPUTE_GOOGLEAPIS_COM_INSTANCE"
| traverse "*", "GCP_COMPUTE_GOOGLEAPIS_COM_SUBNETWORK"
| fields name, gcp.project.id

GCP entities use "*" as the relationship name in traversals because GCP entities do not have named relationship types. Use fieldsKeep to carry fields through traversals and dt.traverse.history[-N] to access ancestor fields.

Pattern 4: Label-Based Ownership

Group resources by GCP labels for ownership and organizational tracking:

smartscapeNodes "GCP_*"
| filter isNotNull(`tags:gcp_labels`)
| fields name, gcp.project.id, `tags:gcp_labels`

GCP labels are exposed via the tags:gcp_labels field and must be accessed using backtick syntax. Replace "GCP_*" with a specific type to scope to one service.

---

Reference Guide

Load reference files for detailed queries when the core patterns above need service-specific adaptation.

ReferenceWhen to loadKey content
compute-instances.mdCompute Engine VMs, machine types, IP addresses, disksInstance inventory, machine type distribution, status checks
kubernetes-gke.mdGKE clusters, node pools, pods, deployments, services, RBACCluster topology, workload distribution, RBAC bindings
serverless-containers.mdCloud Run services, revisions, executionsService inventory, revision tracking, execution analysis
networking-dns.mdVPC networks, subnets, routes, DNS recordsNetwork topology, subnet analysis, route tables, DNS record sets
messaging-pubsub.mdPub/Sub topicsTopic inventory, messaging topology
iam-security.mdIAM service accounts, roles, Secret ManagerService account audit, role analysis, secret version tracking
monitoring-logging.mdMonitoring dashboards, logging, saved queriesDashboard inventory, saved query analysis
resource-management.mdGCP projects, regions, organizational hierarchyProject inventory, region distribution, hierarchy mapping
resource-ownership.mdResource ownership, GCP labels, organizational structureLabel-based grouping, project-level summaries, chargeback

---

Best Practices

Configuration Parsing

1. Always parse gcp.object with JSON parser: parse gcp.object, "JSON:gcpjson" 2. Access primary resource attributes via gcpjson[configuration][resource][...] 3. Access extended properties via gcpjson[configuration][additionalAttributes][...] 4. Check for null values after parsing with isNotNull()

GCP Hierarchy

1. Organization → Folder → Project → Region/Zone 2. Use gcp.project.id as the primary scoping filter 3. Use gcp.organization.id for cross-project queries 4. Use gcp.region and gcp.zone for location-based analysis

Entity Naming

1. Entity types follow the GCP_<SERVICE_API>_<RESOURCE> format 2. Service API maps to the Google API domain with underscores replacing dots and hyphens 3. Use specific entity types (avoid "GCP_*" wildcards when possible)

Labels

1. GCP labels must be accessed via backtick syntax: ` tags:gcp_labels 2. Use isNotNull(tags:gcp_labels)` for label-based filtering 3. Track label coverage with summarize operations

Relationship Traversal

1. Use "*" as the relationship name — GCP entities do not have named relationship types 2. Use fieldsKeep to maintain important fields through traversal 3. Access traversal history with dt.traverse.history[-N] 4. Complex topologies may require multiple traverse operations

---

Limitations and Notes

Smartscape Limitations

  • Smartscape data reflects the most recent scan; there may be a delay between GCP changes and Dynatrace visibility
  • Not all GCP services are represented as entity types
  • Some configuration fields may be null depending on resource setup
  • Resource discovery depends on GCP integration configuration

GCP-Specific Notes

  • GCP labels must be accessed via backtick syntax: ` tags:gcp_labels `
  • GCP entities use "*" for relationship traversal (no named relationship types)
  • GCP object configuration requires parsing with parse gcp.object, "JSON:gcpjson"
  • Configuration fields nest under gcpjson[configuration][resource][...] (differs from AWS pattern)

General Tips

  • Filter early by project and region for better performance
  • Use isNotNull() and isNull() for graceful null handling
  • Combine project and region filters for large environments
  • Use countDistinct() for unique resource counts
  • Limit results with | limit N during exploration

Related skills

FAQ

Which GCP services does dt-obs-gcp cover?

dt-obs-gcp configures Dynatrace observability for Google Kubernetes Engine (GKE), Cloud Run, and native GCP service integrations. The skill sets up dashboards, SLOs, and AI workload telemetry across these deployment targets.

When should developers use dt-obs-gcp?

dt-obs-gcp applies when production or staging AI workloads run on Google Cloud and need Dynatrace full-stack visibility beyond default Cloud Monitoring—especially GKE clusters and Cloud Run services requiring SLO tracking.

Cloud & Infrastructuremonitoringinfra

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