Operations | Monitoring | ITSM | DevOps | Cloud

It’s no secret that AWS Lambda adoption has grown steadily since AWS first released it in 2015—and for good reason. The benefits of adopting Lambda are many: leveraging Lambda eliminates the need to provision and manage servers, enabling teams to just focus on their code without the mental and operational overhead of worrying about the underlying infrastructure.

Large-scale cloud applications are usually built using interconnected services that can be rather hard to troubleshoot. When a service is scaled, simple logging doesn’t cut it anymore and a more in-depth view into system’s flow is required. That’s where distributed tracing comes into play; it allows developers and SREs to get a detailed view of a request as it travels through the system of services.

Here at Moogsoft, we take quality seriously and one of the most important goals for our test suites is to catch issues early on in the development process. A lot of our automated tests are integrated into our CI/CD (Continuous Integration/Continuous Deployment) pipeline as gates that can block a merge request with quality issues. Therefore, to ensure stable CI/CD pipelines as well as quick and quality releases to production, it is important to have tests that are stable and lightweight.

At Cloudsmith, we pride ourselves in being the central source of truth for your packages and build artifacts. We provide one global platform to centralize your assets and give you the control and visibility that you need as part of a modern DevOps process.

If you were thinking that nothing will change in your VMware data centre in the following years, think again. Data centre storage is experiencing a paradigm shift. Software-defined storage solutions, such as Ceph, bring flexibility and reduce operational costs. As a result, Ceph storage on VMware has the potential to revolutionise VMware clusters where SAN (Storage Area Network) was the incumbent for many years.

Kubeflow Pipelines are a great way to build portable, scalable machine learning workflows. It is one part of a larger Kubeflow ecosystem that aims to reduce the complexity and time involved with training and deploying machine learning models at scale. In this blog series, we demystify Kubeflow pipelines and showcase this method to produce reusable and reproducible data science.

An SCM such as Git is more than just a database for source code. It’s not only the thing you need to interact with to get code to production, but also a log of changes on a project. It’s not just the last couple of weeks of commits that are worth looking at. Any commit remains relevant weeks, months and years later. A commit serves multiple purposes. The first one is to explain a change during its review and the second is to explain a change to a future reader.