Engineering organizations that ship fast have Observability as part of their core DNA.

At Traceloop, we’re solving the single thing engineers hate most: writing tests for their code. More specifically, writing tests for complex systems with lots of side effects, such as this imaginary one, which is still a lot simpler than most architectures I’ve seen: As you can see, when an API call is made to a service, there are a lot of things happening asynchronously in the backend; some are even conditional.

In the not-too-distant past, the debate was between on-prem and cloud-native. You’re now faced with the choice of choosing between the different cloud infrastructure providers, and inevitably, someone will throw in the phrase “vendor lock-in”. And not having a response for the famed “vendor-lockin” sometimes leads to building things that are much more complex than required basis the stage that the product is in.

Just when you thought everything that could be shifted left has been shifted left, we’re sorry to say you’ve missed something: observability. Modern software development—where code is shipped fast and fixed quickly—simply can’t happen without building observability in before deployments happen. Teams need to see inside the code and CI/CD pipelines before anything ships, because finding problems early makes them easier to fix.

There’s a new term on the technology block: Applied Observability. Gartner estimates that 70% of organizations will successfully adopt applied observability capabilities in coming years. The most common use cases of applied observability will include: But exactly what is applied observability? We’ve got the answers and more here for you to get a full understanding. Read on!

Understanding Metrics, Logs, Events and Traces - the key pillars of observability and their pros and cons for SRE and DevOps teams.

As more organizations leverage the Amazon Web Services (AWS) cloud platform and services to drive operational efficiency and bring products to market, managing logs becomes a critical component of maintaining visibility and safeguarding multi-account AWS environments. Traditionally, logs are stored in Amazon Simple Storage Service (Amazon S3) and then shipped to an external monitoring and analysis solution for further processing.

Datadog and Splunk are among the most popular performance monitoring tools available on the market. If you’re looking for such a solution and looking to scratch one off your shortlist, look no further than this article. In this Datadog vs Splunk comparison, we will take a deep dive into everything each tool has to offer. We will point out their similarities and differences to help you decide which tool can meet your needs better.

Honeycomb recently released our Query Assistant, which uses ChatGPT behind the scenes to build queries based on your natural language question. It's pretty cool. While developing this feature, our team (including Tanya Romankova and Craig Atkinson) built tracing in from the start, and used it to get the feature working smoothly. Here's an example. This trace shows a Query Assistant call that took 14 seconds. Is ChatGPT that slow? Our traces can tell us!