In a rapidly expanding, highly distributed cloud infrastructure environment, it can be difficult to make decisions about the design and management of cloud architectures. That’s because it’s hard for a single observer to see the full scope when their organization owns thousands of cloud resources distributed across hundreds of accounts. You need broad, complete visibility in order to find underutilized resources and other forms of bloat.

Modern distributed applications are composed of potentially hundreds of disparate services, all containing code from different internal development teams as well as from third-party libraries and frameworks with limited external visibility. Instrumenting your code is essential for ensuring the operational excellence of all these different services. However, keeping your instrumentation up to date can be challenging when new issues arise outside the scope of your existing logs.

The Datadog Service Catalog consolidates knowledge of your organization’s services and shows you information about their performance, reliability, and ownership in a central location. The Service Catalog now includes Service Scorecards, which inform service owners, SREs, and other stakeholders throughout your organization of any gaps in observability or deviations from reliability best practices.

IT environments can produce billions of log events each day from a variety of hosts and applications. Collecting this data can be costly, often resulting in increased network overhead from processing inefficiencies and inconsistent ingestion during major system events. Google Cloud Dataflow is a serverless, fully managed framework that enables you to automate and autoscale data processing.

CloudNatix is an infrastructure monitoring and optimization platform for VMs, containers, and other cloud resources. Customers can use CloudNatix’s Autopilot feature to automatically configure and run infrastructure optimization workflows that allocate and run their resources more efficiently. CloudNatix can take action to auto-size Kubernetes and VM workloads, defragment Kubernetes clusters, and create harvest pods from unused VMs, among other key optimizations.

It can be hard to figure out why response times are high in Java applications. In my experience, when engineers investigate this type of issue, they typically use one of two methods: They either apply a process of elimination to find a recent commit that might have caused the problem, or they use profiles of the system to look for the cause of value changes in relevant metrics.