In order to grow your organization effectively, you need to ensure the scalability of your systems. In a broad, distributed architecture, critical processes like incident triage, security response, and large-scale configuration changes can be difficult to execute without a programmatically accessible registry of what’s running in production and who owns it.

Debugging errors in production environments can frustrate your team and disrupt your development cycle. Once error tracking detects an exception, you then need to identify which specific line of code or module is responsible for the error. Without access to the inputs and associated states that caused the errors, reproducing them to find the root cause and a solution can be a lengthy and challenging process.

Container-native technologies increase the scalability and speed of deployment offered by containerized infrastructure, but they also present new monitoring challenges for organizations that adopt them. For example, because containers are ephemeral and share resources, tracking resource provisioning in container-native tools is essential to ensure consistent application performance.

Funnels can be powerful tools for analyzing your UX, but figuring out exactly which user journeys you want to study can be challenging. Even if you have an ideal journey in mind, users often take steps you don’t expect. As a result, your funnels—and therefore, your optimization efforts—can easily miss the most influential pages in your application. Indeed, how do you build the best possible funnel when there are thousands of paths users can take after any given page?

Visibility into the production performance of code iterations helps developers verify that application releases and updates are working as intended. However, when variables such as large-scale user requests and increased server load create issues that were absent during testing, developers will often need to pivot from investigating production data back to their coding environment to address errors and vulnerabilities.

Processes—the service workloads that run on your infrastructure—are the building blocks of your application, and it’s critical to know how well they operate at every level of the stack. Degraded process performance can lead to downtime for your mission-critical services, resulting in loss of customer trust and potentially impacting revenue for the business.

So far in this series, we’ve walked through key etcd metrics and tools you can use to monitor etcd metrics and logs. In this post, we’ll show you how you can monitor etcd with Datadog, including how to: But first, we’ll show you how to set up and configure the Datadog Agent and Cluster Agent to send etcd monitoring data to your Datadog account.

In Part 1 of this series, we looked at how etcd works and the role it plays in managing the state of a Kubernetes cluster. We also explored key etcd metrics you should monitor to ensure the health and performance of your etcd cluster. In this post, we’ll show you how you can use tools like Prometheus, Grafana, and etcdctl to collect and visualize etcd metrics. We’ll also show you how to collect etcd logs that provide context for those metrics.

Etcd is a distributed key-value data store that provides highly available, durable storage for distributed applications. In Kubernetes, etcd functions as part of the control plane, storing data about the actual and desired state of the resources in a cluster. Kubernetes controllers use etcd’s data to reconcile the cluster’s actual state to its desired state. This series focuses on monitoring etcd in Kubernetes.

The Windows Registry is a centralized key-value database that stores permissions, user data, and configuration settings for the Windows operating system and many Windows native applications. The keys stored in the registry provide a granular view into the processes occurring on a Windows host, such as certificate expirations, security checks, and pending reboots.