Editor’s note: This is a follow up to a recent post on how to use Cloud Logging with containerized applications running in Google Kubernetes Engine. In this post, we’ll focus on how DevOps teams can use Cloud Monitoring and Logging to find issues quickly. Running containerized apps on Google Kubernetes Engine (GKE) is a way for a DevOps team to focus on developing apps, rather than on the operational tasks required to run a secure, scalable and highly available Kubernetes cluster.

Whether you’re a developer debugging an application or on the DevOps team monitoring applications across several production clusters, logs are the lifeblood of the IT organization. And if you run on top of Google Kubernetes Engine (GKE), you can use Cloud Logging, one of the many services integrated into GKE, to find that useful information. Cloud Logging, and its companion tool Cloud Monitoring, are full featured products that are both deeply integrated into GKE.

We’ve heard from our customers that you need visibility into metrics and logs from Google Cloud, other clouds, and on-prem in one place. Google Cloud has partnered with Blue Medora to bring you a single solution to save time and money in managing your logs in a single place. Google Cloud’s operations management suite gives you the same scalable core platform that powers all internal and Google Cloud observability.

Monitoring your cloud infrastructure is an essential part of making sure your operations are running smoothly. Since announcing the new Cloud Logging interface in February, we’ve heard from users that the new interface is making it faster and easier to meet logging needs, including troubleshooting issues, verifying deployments, and ensuring compliance. One of those users, Arne Claus, is a site reliability engineer at trivago, and has taken advantage of the new interface already.

Data pipelines provide the ability to operate on streams of real-time data and process large data volumes. Monitoring data pipelines can present a challenge because many of the important metrics are unique. For example, with data pipelines, you need to understand the throughput of the pipeline, how long it takes data to flow through it and whether your data pipeline is resource-constrained.