Companies across a wide range of industries rely on Google Cloud Platform (GCP) to run their infrastructure and global-scale applications—from start-ups looking to reduce upfront infrastructure cost, to enterprises aiming to build and release products to market faster. GCP provides the ability to deploy distributed, scalable infrastructure, but it also introduces new types of complexity as companies need visibility into every layer of their dynamic infrastructure to efficiently diagnose performance issues.
Datadog collects and unifies all of the data streaming from these complex environments, with extensive support for GCP services through easy-to-install integrations. Teams can also deploy the Datadog Agent directly on their hosts and compute instances to collect metrics with greater granularity—down to one-second resolution. And with Datadog's out-of-the-box integration dashboards, teams get a high-level view into each and every GCP service.
When migrating to GCP or other cloud platforms, companies often refactor their applications to leverage the flexibility and scalability of the cloud and decrease friction during the process. Datadog enables teams to seamlessly monitor the performance of their updated and legacy services side-by-side in every stage of a migration, so they can ensure expected benchmarks are met. And with 400+ integrations, Datadog can monitor even more of the application services companies rely on during a migration, including those not deployed on GCP.
The Host Map enables teams to monitor real-time data such as network throughput and CPU utilization for all hosts across availability zones to visualize performance before, during, and after a migration.
And with machine learning features such as forecasting and anomaly detection, teams can resolve application issues before they significantly impact customers. For instance, teams can analyze memory usage trends for newly migrated GCE instances and scale their capacity accordingly.
As organizations move workloads from on-premise to hybrid or multi-cloud environments, they often use multiple monitoring tools for end-to-end visibility. This can create data silos and limits teams in their ability to successfully migrate to another platform and monitor issues. Datadog unifies observability data from any host and service, providing deep, cross-platform visibility into critical applications. With Datadog's Service Map, teams can visualize the dependencies between databases, APIs, containers, and more, enabling them to easily follow the data streaming from on-premise to GCP or multi-cloud architectures.
Ephemeral infrastructure platforms such as GKE and Google Cloud Functions can rapidly auto-scale to support application traffic, creating new resources to meet demand or downsizing them to save on costs. Legacy monitoring tools often require additional configuration to capture data from these dynamic environments. Datadog integrates with services like Google Cloud Run to collect data in real time, and automatically scales with GCP infrastructure by monitoring resources as soon as they are created. And with native integrations for services such as Google Cloud Deployment Manager and Ansible, organizations can leverage their existing workflows to deploy and configure Datadog on new hosts, clusters, and other application resources instantly.
Teams need the ability to share critical information and context across their organization to strengthen internal buy-in for adopting GCP services. This single source of truth also instills confidence in an organization's ability to quickly resolve incidents. Datadog offers a wide range of collaboration-friendly tools that every team within an organization can use to review and share information, including integrations for PagerDuty, Slack, JIRA, and more. This enables them to easily see the connections between their application services, collaborate on real-time data, and troubleshoot performance issues faster.