Today, AWS announced enhancements for AWS Distro for OpenTelemetry. We’re working with AWS to build in additional support from partners. In tandem with that launch, Honeycomb is announcing support for event ingestion using OpenTelemetry Protocol (OTLP). With that change, you can simplify management overhead and configuration by no longer needing to maintain a separate OpenTelemetry exporter.
First of all: apologies...because although the title suggests otherwise, this blog is not about "Observability VS. Monitoring." It's a great misconception that observability is this hot new technology that's here to replace your monitoring tools.
There’s an AI-led developer and operations (DevOps) evolution afoot which is stoking SREs’ increasingly critical efforts to assure and improve the customer experience by automating the toil out of observability. This movement feeds on a supercharged process of turning telemetry into actionable insight by automatically drawing anomalies, changes and events out of the full-stack event and telemetry data, and analyzing it for correlation and causality.
Elasticsearch is a great platform for any data lake initiative, and ideal for analyzing your monitoring and observability data. But if you’re working with a number of different monitoring and observability tools, especially across multiple cloud environments, you might find it challenging to get all your data into Elasticsearch.
Breaking down larger, monolithic software, services, and applications into microservices has become a standard practice for developers. While this solves many issues, it also creates new ones. Architectures composed of microservices create their own unique challenges. In this article, we are going to break down some of the most common. More specifically, we are going to assess how observability-based solutions can overcome many of these obstacles.
As a concept, observability has been a relatively recent entrant into the world of information technology and cloud technology. The idea originated initially from controls system engineering. Observability refers to the concept of inferring the internal status of the system based on the outputs derived from the same. This is the conventional definition of observability.
‘Tis the season to set 2021 goals. Whether setting OKRs, KPIs, KPAs, MBOs, or any other flavor of goal-setting frameworks in an endless sea of acronym soup, chances are that you’re still dealing with a sizable disconnect between business objectives and daily engineering work. Service Level Objectives (SLOs) have boomed in popularity because they provide a common language between business stakeholders and engineers to set aligned goals.
The ability to access the internal state of your application ecosystem is critical to optimizing your applications and the experience of your users. Elastic Cloud on Microsoft Azure gives you access to Elastic Observability, allowing you to monitor your infrastructure and see how every signal interrelates by utilizing a wide variety of resources that can be deployed in minutes.
My team was not happy with me. I had just convened a meeting of my direct reports — and the managers that reported to them — to deliver the news personally. “No more new tools,” I told them. “We have everything we need to do our job. Our environment just doesn’t change that fast. So stop bringing me requests for new tools. The answer is no.” It was unquestionably the right call at the time, but today, it would be laughable.
