Observability is the practice of equipping software and infrastructure with tools capable of gathering actionable data showing not only when an application error or issue occurs but why it occurred. Most traditional monitoring tools gather information passively; observability practices are different. They focus on actively gathering relevant data, especially factors driving operational decisions and actions.

Network observability is critical. You need the ability to answer any question about your network—across clouds, on-prem, edge locations, and user devices—quickly and easily. But network observability is not always easy. To be successful, you need to collect network telemetry, and that telemetry needs to be extensive and diverse. And once you have that raw telemetry data, you need to interpret it.

Observability pipelines solve some critical problems IT is facing today: the cloud environment has generated an unprecedented amount of data in recent years; enterprises now have multiple SaaS/cloud-based applications running; it’s becoming tough to know which of this massive volume of data needs to be processed for analysis vs. stored (often for regulatory reasons) cheaply; and dealing with growing numbers of source data makes the meaningful management of the problem only harder.

Data visualizations take complex information and present it in a clean and easy-to-understand visual. Done right, they can allow quick insight through easy pattern and outlier recognition. Done wrong, it can confuse, obfuscate, and lead to wrong conclusions. Yikes! Over the past few months, we've been hard at work modernizing Honeycomb’s data visualizations to address consistency issues, confusing displays, access to settings, and to improve their overall look and feel.

Artificial intelligence for IT Operations (or AIOps) has been playing an expanding role in helping SREs, DevOps, and developers effectively navigate the challenges around application and infrastructure complexity, pace of change, and data volume that characterize the operations landscape.

In today’s fast paced and constantly evolving digital landscape, observability has become a critical component of effective software development. Companies are relying more on and using machine and telemetry data to fix customer problems, refine software and applications, and enhance security. However, while more data has empowered teams with more insights, the value derived from that data isn’t keeping pace with this growth. So how can these teams derive more value from telemetry data?

The software industry has seen many evolutions. There is a new disruption in the market every five years or so. Software testing cannot remain isolated from all the latest trends and technologies. Testing strategies need to keep up with agile development, faster deployments and increasing customer demand for reliability and user-friendly interfacing. They need to be able to grow just as quickly and just as reliably as the business logic.

How is your organization handling Kubernetes observability? What tools are you using to monitor Kubernetes? Is it a time-consuming, manual process to collect, store and visualize your logging, metrics and tracing data? And, what are you actually getting out of all that investment? At Logz.io we’re trying to make this process easier for customers who are serious about Kubernetes observability. We’ve made significant investments in this area for Kubernetes use cases.