The latest News and Information on Observabilty for complex systems and related technologies.
Integration is a fundamental part of any IT infrastructure. It allows organizations to connect different systems and applications together in order to share data and information. As organizations become more complex and interconnected, they need to ensure they have complete observability and monitoring of their integration architecture. This is essential in order to discover, understand and fix any issues that can arise.
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.
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?