In the rapidly evolving landscape of modern tech, system reliability has become a critical factor for businesses to succeed. To ensure the stability and performance of complex distributed systems, companies are relying on observability—a concept that isn’t synonymous, but instead goes beyond traditional monitoring approaches.

As we’ve shown in previous blogs, Elastic® provides a way to ingest and manage telemetry from the Kubernetes cluster and the application running on it. Elastic provides out-of-the-box dashboards to help with tracking metrics, log management and analytics, APM functionality (which also supports native OpenTelemetry), and the ability to analyze everything with AIOps features and machine learning (ML).

There’s a reason everyone dreads debugging, especially in today’s complex cloud systems: it’s at the high stakes nexus of nervous senior management, overworked engineers, neverending rabbit holes, copious buckets of time, and fickle customers.

We’ve known for years that context switching is detrimental to productivity. Both computers and humans become less productive with each additional concurrent task or priority. Every time you need to shift your focus between projects, you lose approximately 20% efficiency as you figure out where you left off, what needs to be done, how the work fits into the project, etc.

Observability has become a critical aspect of modern software development and operations, allowing organizations to gain insights into the health and performance of their applications and systems. One of the key decisions when implementing observability is choosing between commercial or open-source tools. We spoke to several professionals who shared their experiences and insights on this topic, shedding light on the pros and cons of each approach.

The Honeycomb design team began work on Lattice in early 2021. Over several months, we worked to clean up and optimize typography, color, spacing, and many other product experience areas. We conducted an extensive audit of all components, documenting design inconsistencies and laying the foundation for a sustainable design system. However, a more extensive evaluation and audit were necessary before updating or developing components.

Enterprises have enough data, in fact, they are overwhelmed with it, but finding the nuggets of value amongst the data ‘noise’ is not all that simple. It is bucket’d, blob’d, and bestrewn across the enterprise infrastructure in clouds, filesystems, and hosts machines. It’s logs, metrics, traces, config files, and more, but as Jimmy Buffett says, “we’ve all got ’em, we all want ’em, but what do we do with ’em”.

Wikipedia defines smoke testing as “preliminary testing to reveal simple failures severe enough to, for example, reject a prospective software release.” Also known as confidence testing, smoke testing is intended to focus on some critical aspects of the software that are required as a baseline.

Adoption of Azure Functions in cloud-native applications on Microsoft Azure has been increasing exponentially over the last few years. Serverless functions, such as the Azure Functions, provide a high level of abstraction from the underlying infrastructure and orchestration, given these tasks are managed by the cloud provider. Software development teams can then focus on the implementation of business and application logic.

Technology is a fast-moving commodity. Trends, thoughts, techniques, and tools evolve rapidly in the software technology space. This rapid change is particularly felt in the software the engineers in the cloud-native space make use of to build, deploy, and operate their applications. One particular area where we see rapid evolution in the past few years/months is Observability.