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Use this guide to get started monitoring your Google Cloud environment at a folder or organization level. This approach simplifies the setup for Google Cloud environments with multiple projects, ensuring that there are no gaps in monitoring.
◆ If your organization restricts identities by domain, you must add Datadog’s customer identity C0147pk0i as an allowed value in your policy.
◆ The Google Cloud integration requires the below APIs to be enabled for the folder or organization you want to monitor:
Org-level (or folder-level) monitoring is recommended for comprehensive coverage of all projects, including any future projects that may be created in an org or folder. To set up monitoring for individual projects, see the main Google Cloud integration page.
Note: You must have the Admin role assigned to your Cloud Identity user account at the desired scope (for example, Organization Admin).
Note: The Browser role is only required in the default project of the service account. Other projects require only the other listed roles.
Note: If you previously configured access using a shared Datadog principal, you can revoke the permission for that principal after you complete these steps.
Note: Keep this window open for Section 4.
<SA_NAME>@<PROJECT_ID>.iam.gserviceaccount.com.After finishing these steps, metrics appear in Datadog after approximately 15 minutes.
To view your metrics, use the left menu to navigate to Metrics > Summary and search for gcp:
The Google Cloud integration collects all available Google Cloud metrics from your projects through the Google Cloud Monitoring API. Integrations are installed automatically when Datadog recognizes data being ingested in from your Google Cloud account. <EXAMPLE?>
| Integration | Description |
|---|---|
| App Engine | PaaS (platform as a service) to build scalable applications |
| Big Query | Enterprise data warehouse |
| Bigtable | NoSQL Big Data database service |
| Cloud SQL | MySQL database service |
| Cloud APIs | Programmatic interfaces for all Google Cloud Platform services |
| Cloud Armor | Network security service to help protect against denial of service and web attacks |
| Cloud Composer | A fully managed workflow orchestration service |
| Cloud Dataproc | A cloud service for running Apache Spark and Apache Hadoop clusters |
| Cloud Dataflow | A fully-managed service for transforming and enriching data in stream and batch modes |
| Cloud Filestore | High-performance, fully managed file storage |
| Cloud Firestore | A flexible, scalable database for mobile, web, and server development |
| Cloud Interconnect | Hybrid connectivity |
| Cloud IoT | Secure device connection and management |
| Cloud Load Balancing | Distribute load-balanced compute resources |
| Cloud Logging | Real-time log management and analysis |
| Cloud Memorystore for Redis | A fully managed in-memory data store service |
| Cloud Router | Exchange routes between your VPC and on-premises networks by using BGP |
| Cloud Run | Managed compute platform that runs stateless containers through HTTP |
| Cloud Security Command Center | Security Command Center is a threat reporting service. |
| Cloud Tasks | Distributed task queues |
| Cloud TPU | Train and run machine learning models |
| Compute Engine | High performance virtual machines |
| Container Engine | Kubernetes, managed by google |
| Datastore | NoSQL database |
| Firebase | Mobile platform for application development |
| Functions | Serverless platform for building event-based microservices |
| Kubernetes Engine | Cluster manager and orchestration system |
| Machine Learning | Machine learning services |
| Private Service Connect | Access managed services with private VPC connections |
| Pub/Sub | Real-time messaging service |
| Spanner | Horizontally scalable, globally consistent, relational database service |
| Storage | Unified object storage |
| Vertex AI | Build, train and deploy custom machine learning (ML) models. |
| VPN | Managed network functionality |
For deep dives into monitoring many of the more popular services, check out the blogs linked below.
You can limit metric collection to only the specific hosts, Cloud Run instances, or Google Cloud integrations valuable to your organization. This can help control costs by reducing the number of API calls made on your behalf.
Under the Metric Collection tab in Datadog’s Google Cloud integration page, deselect the metric namespaces to exclude.
datadog:true) to the hosts or Cloud Run instances you want to monitor with Datadog.? for single character, * for multi-character) to match many hosts, or ! to exclude certain hosts. This example includes all c1* sized instances, but excludes staging hosts:datadog:monitored,env:production,!env:staging,instance-type:c1.*
See Google’s documentation on Creating and managing labels for more details.
In the below example, only Google Cloud hosts with the label datadog:true are monitored by Datadog:
Forwarding logs from your Google Cloud environment enables near real-time monitoring of the resources and activities taking place in your organization or folder. You can set up log monitors to be notified of issues, use Cloud SIEM to detect threats, or leverage Watchdog to identify unknown issues or anomalous behavior.
Use the Datadog Dataflow template to batch and compresses your log events before forwarding them to Datadog through Google Cloud Dataflow. This is the most network-efficient way to forward your logs. To specify which logs are forwarded, configure the Google Cloud Logging sink with any inclusion or exclusion queries using Google Cloud’s Logging query language.
Follow the instructions listed here to set up Log Collection. You can also use the Stream logs from Google Cloud to Datadog guide in the Google Cloud architecture center, for a more detailed explanation of the steps and architecture involved in log forwarding. For a deep dive into the benefits of the Pub/Sub to Datadog template, read Stream your Google Cloud logs to Datadog with Dataflow in the Datadog blog.
Resource changes collection allows you to monitor changes in your Google Cloud environment. You receive resource events in Datadog when Google’s Cloud Asset Inventory detects changes in your cloud resources. These events are forwarded to Datadog through a Cloud Pub/Sub topic and subscription.
For detailed setup instructions, see the resource changes collection section of the Google Cloud integration documentation.
After the Google Cloud integration is configured, Datadog automatically starts collecting Google Cloud metrics. However, you can leverage the Datadog Agent to gather deeper insights into your infrastructure.
The Datadog Agent provides the most granular, low-latency metrics from your infrastructure, delivering real-time insights into CPU, memory, disk usage, and more for your Google Cloud hosts. The Agent can be installed on any host, including GKE.
The Agent also supports a wide range of integrations, enabling you to extend visibility into specific services and databases running on your hosts.
Traces collected through the Agent enable comprehensive Application Performance Monitoring (APM), helping you understand end-to-end service performance.
Logs collected through the Agent provide visibility into your Google Cloud resources, and the activities taking place in your Google Cloud environment.
For the full list of benefits of installing the Agent on your cloud instances, see Why should I install the Datadog Agent on my cloud instances?
Use the Google Cloud Private Service Connect integration to visualize connections, data transferred, and dropped packets through Private Service Connect. This gives you visibility into important metrics from your Private Service Connect connections, both for producers as well as consumers. Private Service Connect (PSC) is a Google Cloud networking product that enables you to access Google Cloud services, third-party partner services, and company-owned applications directly from your Virtual Private Cloud (VPC).
See Access Datadog privately and monitor your Google Cloud Private Service Connect usage in the Datadog blog for more information.
Use the Google Cloud Run integration to get detailed information on your Cloud Run containers, such as metrics and audit logs.
Datadog’s Google Cloud Cost Management provides insights for engineering and finance teams to understand how infrastructure changes impact costs, allocate spend across your organization, and identify potential improvements.
Cloud SIEM provides real-time analysis of operational and security logs, while using out-of-the-box integrations and rules to detect and investigate threats. To use this feature, see Getting Started with Cloud SIEM.
To view security findings from Google Cloud Security Command Center in Cloud SIEM, toggle the Enable collection of security findings option under the Security Findings tab & follow the setup instructions on the Google Cloud Security Command Center guide.
Datadog Cloud Security Management (CSM) delivers real-time threat detection and continuous configuration audits across your entire cloud infrastructure. Check out the Setting up Cloud Security Management guide to get started.
After setting up CSM, toggle the Enable Resource Collection option under the Resource Collection tab to start collecting configuration data for the Resource Catalog and CSM. Then, follow these instructions to enable Misconfigurations and Identity Risks (CIEM) on Google Cloud.
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