Developers and SREs choose to host their applications on Google Cloud Platform (GCP) for its reliability, speed, and ease of use. On Google Cloud, development teams are finding additional value in migrating to Kubernetes on GKE, leveraging the latest serverless options like Cloud Run, and improving traditional, tiered applications with managed services.
Elastic Observability offers 16 out-of-the-box integrations for Google Cloud services with more on the way. A full list of Google Cloud integrations can be found in our online documentation.
In addition to our native Google Cloud integrations, Elastic Observability aggregates not only logs but also metrics for Google Cloud services and the applications running on Google Cloud compute services (Compute Engine, Cloud Run, Cloud Functions, Kubernetes Engine). All this data can be analyzed visually and more intuitively using Elastic®’s advanced machine learning (ML) capabilities, which help detect performance issues and surface root causes before end users are affected.
For more details on how Elastic Observability provides application performance monitoring (APM) capabilities such as service maps, tracing, dependencies, and ML based metrics correlations, read: APM correlations in Elastic Observability: Automatically identifying probable causes of slow or failed transactions.
That’s right, Elastic offers metrics ingest, aggregation, and analysis for Google Cloud services and applications on Google Cloud compute services. Elastic is more than logs — it offers a unified observability solution for Google Cloud environments.
In this blog, I’ll review how Elastic Observability can monitor metrics for a three-tier web application running on Google Cloud services, which include:
- Google Cloud Run
- Google Cloud SQL for PostgreSQL
- Google Cloud Memorystore for Redis
- Google Cloud VPC Network
As you will see, once the integration is installed, metrics will arrive instantly and you can immediately start reviewing metrics.
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