When our CEO and co-founder Tomer Levy delivered his “Observability is Broken” presentation at last year’s AWS re:Invent, he highlighted numerous challenges faced by today’s organizations as they seek to advance their observability practices. Of the six individual points that he noted, two specifically dealt with the current shortage of available engineering expertise, with another two focused on data overload.
Technology juggernauts–despite their larger staffs and budgets–still face the “cognitive load” for DevOps that many organizations deal with day-to-day. That’s what led Spotify to build Backstage, which supports DevOps and platform engineering practices for the creation of developer portals.
The platform development team at Endeavor Streaming has a critical mission — from balancing operation of the company’s leading digital video platform, at scale, to ensuring everything in their complex cloud environment is performing as expected. Enabling the company to confidently build on top of its platform and continue to evolve their product delivery is thereby also dependent on maintaining detailed visibility into its supporting cloud applications and infrastructure.
At Logz.io, we’re seeing a very fast pace of adoption for Kubernetes–at this point, it’s even outpacing cloud adoption, with companies running on-prem fully adopting Kubernetes in production. Why are companies going in this direction? Kubernetes provides additional layers of abstraction, which helps create business agility and flexibility for deploying critical applications. At the same time, those abstraction layers create additional complexity for observability.
The observability landscape is changing fast, as organizations look to deploy applications and separate themselves from competition at a breakneck pace. What are the trends organizations need to be aware of as they make sense of the landscape? Every year, we at Logz.io set out to answer this question by going right to the DevOps and observability practitioners on the front lines.
Managing observability data can feel like a juggling act. Modern cloud applications generate vast amounts of data, and quickly accessing the most important data is a fundamental step toward quickly gaining unobstructed visibility into your infrastructure and applications. Yet, when data volumes grow, complexity follows. Many observability users find it overwhelming to assess the critical data generated from their complex infrastructure and applications.
Cost optimization has been one of the hottest topics in observability (and beyond!) lately. Everyone is striving to be efficient, spend money wisely, and get the most out of every dollar invested. At Logz.io, we recently embarked on a very interesting and fruitful data volume optimization journey, reducing our own internal log volume by a whopping 50%. In this article, I’ll tell you how exactly we achieved this result.
