Operations | Monitoring | ITSM | DevOps | Cloud

Deploying and monitoring performance for an entire Kubernetes cluster can be complex. To simplify the process, we’ve added service discovery functionality to eliminate complex configuration, in addition to more advanced monitoring for viewing activity inside containers. Service discovery identifies k8s pods running on a cluster and immediately starts monitoring system performance. All containers are identified, regardless of complexity.

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.

Edge computing is likely the most interesting section of the broader world of IoT. If IoT is about connecting all the devices to the Internet, edge computing is about giving more processing power to devices at the edge. Edge computing views these edge devices as mini clouds or mini data centers. They each have their own mini servers, mini networking, mini storage, apps running on top of this infrastructure, and endpoint devices.

PHP is one of the most popular programming languages on the web. It powers many widely used content management systems like WordPress and Drupal, and provides the backbone for modern server-side frameworks like Laravel and Symfony. Despite its popularity, PHP has a bit of a reputation for being slow and hard to maintain. It has gotten better in recent years, but there are two features that high-performance PHP applications will likely need: OPcache and PHP FastCGI Process Manager (PHP-FPM).

The number of publicly available Helm charts is continuously growing and while this is great for the community, it can be challenging to navigate the vast sea of Helm charts and Helm chart repositories. Like a ship’s captain, you need more than just a list of where you can go, but the details to ensure those under your charge arrive certainly and safely. Not just what can be seen on the surface, but what lies underneath, and the hazards that await.

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.