Operations | Monitoring | ITSM | DevOps | Cloud

In previous blogs, we explored how Elastic Observability can help you monitor various AWS services and analyze them effectively: One of the more heavily used AWS container services is Amazon ECS (Elastic Container Service). While there is a trend toward using Fargate to simplify the setup and management of ECS clusters, many users still prefer using Amazon ECS with EC2 instances.

In today's market, companies undergoing digital transformation require secure and reliable systems to meet customer demands, handle macroeconomic uncertainty and navigate new disruptions. Digital resilience is key to providing an uninterrupted customer experience and adapting to new operating models. Companies that prioritize digital resilience can proactively prevent major issues, absorb shocks to digital systems and accelerate transformations.

Salary Finance is a UK-based financial well-being employee benefit program. Over the last seven years, the company grew from a startup to a scaleup, earning rave reviews along the way from its more than 4,000 customers. However, with fast growth also comes natural growing pains. As their customer base expanded, so did the number of incidents they experienced, which also became harder to diagnose due to lack of visibility into their increasingly complex environment.

This post will discuss remote debugging in VS Code and how to improve the remote debugging experience to maximize debugging productivity for developers. Visual Studio Code, or VS Code, is one of the most popular IDEs. Within ten years of its initial release, VS Code has garnered the top spot among popularity indices, and its community is growing steadily. Developers love VS Code not only for its simplicity but also due to its rich ecosystem of extensions, including the support for debugging.

The W3C trace context specification is an amazing standard and a massive leap in standardization of telemetry correlation in the current climate of microservices being the de facto for new systems (that’s a debate for another day).

Christophe is a self-taught engineer from France who specializes in site reliability engineering. He spends most of his time building systems with open-source technologies. In his free time, Christophe enjoys traveling and discovering new cultures, but he would also settle for a good book by the pool with a lemon sorbet.

If you’re a systems engineer, SRE, or just someone with a love for tech buzzwords, you’ve likely heard about “data lakes”. Before we dive deep into this concept, let’s debunk the illusion: there aren’t any floaties or actual lakes involved! Instead, imagine a vast reservoir where you store loads and loads of raw data in its natural format. Now, pair this with the idea of observability and telemetry pipelines, and we have ourselves an engaging topic.

In this age of complex software systems, code instrumentation patterns define specific approaches to debugging various anomalies in business logic. These approaches offer more options beyond the built-in debuggers to improve developer productivity, ultimately creating a positive impact on the software’s commercial performance. In this post, let’s examine the various code instrumentation patterns for Node.js.

Observability has traditionally been conceptualized in terms of three core facets: logs, metrics, and traces. For years, these elements have been seen as the “pillars” of observability, serving as the foundational components for system monitoring and delivering key insights to improve system performance. However, with the exponential growth in system complexity, a more comprehensive and unified perspective on observability has become necessary.