Operations | Monitoring | ITSM | DevOps | Cloud

Cloud computing and containerization have brought many benefits to IT systems, like speed to market and on-demand scaling. But it has also increased operational complexity. Applications built on dynamic and distributed infrastructure are challenging to operate and maintain. A robust observability framework can help application owners stay on top of their software systems. In this article, we will introduce SigNoz - an open source observability platform.

Armin Müller is the co-founder of ScopeSET. ScopeSET specializes in R&D work to build and integrate tools in the model-based systems engineering domain, with a track record of more than 15 years of delivering innovative solutions for ESA and the aerospace industry. Training machine learning models takes a lot of time, so we’re always looking for ways to accelerate the process at ScopeSET. We use open source components to build research and development tools for technical companies.

When you maintain an internal or public-facing API, the API documentation is an important component of the overall user/developer experience. One of the industry standards for such documentation is the OpenAPI specification. With an OpenAPI specification, you define a contract that specifies how your API should behave, but nothing stops the parties involved from breaking such a contract (e.g., using a wrong implementation or invalid input).

Speedscale creates load tests from recorded traffic so generating load is pretty core to what we do. As a brief overview, we record traffic from your service in one environment and replay it in another, optionally increasing load several fold. During a replay the Speedscale load generator makes requests against the system under test (SUT), with the responses from external dependencies like APIs or a payment processor optionally mocked out for consistency. Your service is the SUT here.

The traditional embedded Linux development model ties applications to the OS. Such a constraint means apps have to target a specific release, which lowers development velocity. Furthermore, broken upgrades in one part of the device may affect refreshes in the rest of the OS. On the other hand, embedded developers are increasingly looking at open-source software to enable rapid app-centric software deployment and global collaboration.

Modern IT environments are so complex, dynamic, and expansive that humans alone cannot effectively manage and maintain them. As a developer and operator, I have had to deal with failed servers and containers, running out of storage space, slow or unreliable network links, bugs in code, and unpredictable workloads in some applications.

Distributed tracing helps you track requests across services and understand issues affecting your application performance. In distributed cloud architecture, debugging performance issues is complicated. Distributed tracing gives visibility to teams on how a user request performs across different services, protocols, and cloud infrastructure. Let’s start with a brief overview of distributed tracing.

In the beginning, there was Amazon Web Services (AWS). And AWS set a standard for cloud computing. AWS was fast, flexible, convenient to use and geo-redundant. Definitely much better than legacy IT infrastructure or VMware. A lot of enterprises all over the world started migrating their business applications to AWS.