When you send telemetry into Honeycomb, our infrastructure needs to buffer your data before processing it in our “retriever” columnar storage database. For the entirety of Honeycomb’s existence, we have used Apache Kafka to perform this buffering function in our observability pipeline.

SaaS is exploding and so it should; it takes commoditized work and infrastructure away from tech teams so that they can focus on differentiating features. But what happens when it goes wrong? How do SaaS platforms make sure they aren't letting their customers down and in turn, letting their customers down? Observability, bolstered with AI gives all the partners the best chance to optimize availability and customer experience. Here's how.

Technology environments are rapidly evolving as organizations look to remain competitive, accelerate innovation and make themselves more agile. But in the process, many of the observers, i.e., stakeholders who track infrastructure and application metrics, are falling behind, unable to monitor and manage modern, cloud-native apps and multi-cloud environments due to the complexity that comes with them.

Achieving full, 360-degree observability across your entire IT ecosystem and application components can be thwarted by the disconnect between technical monitoring and business outcomes - running the risk of catastrophic service failures.

One of the most common questions we get at Honeycomb is “What insights can you get in the browser?” Browser-based code has become orders of magnitude more complex than it used to be. There are many different patterns, and, with the rise of Single Page App frameworks, a lot of the code that is traditionally done in a backend or middle layer is now being pushed up to the browser. Instead, the questions should be: What insights do frontend engineers want?

In the past few years, the word “observability” has steadily gained traction in the discussions around monitoring, DevOps, and, especially, cloud-native computing. However, there is significant confusion about the overlap or difference between observability and monitoring.

It’s one thing to set up an observability strategy. But what’s it like to introduce and scale observability effectively across an organization? In a wide-ranging conversation at ObservabilityCON 2021, three technical pros from Snyk, TripAdvisor, and Citibank joined Grafana Labs VP Global Solutions Engineering Steve Mayzak and — with more than 75 years experience between them — they shared the triumphs and turbulence in their respective observability journeys.

Dear Trapped, Thanks for asking the question! Approaching observability as an all-or-nothing problem often leads to the project feeling daunting. But that’s not specific to observability—any project can be overwhelming if you think it needs to be done all at once, perfectly. Such as, erm, writing an entire book on observability! *looks around worriedly*

Moving beyond traditional monitoring to embrace full stack observability offers a seemingly endless range of benefits. Beyond unifying logs, metrics, and traces in a single platform, the opportunity to enlist advanced analytics and engage a more predictive approach represents another huge step forward.