Operations | Monitoring | ITSM | DevOps | Cloud

For IT Ops people working in organizations with a truly global presence – say managed service providers, banking, finance, aviation and utilities, coherently maintaining full visibility of service health and identifying service-affecting issues can be a big headache. As a global enterprise expands and grows its operations, be it through acquisition, or during the onboarding of new customers, there’s a tendency for multiple incident management and monitoring tools to accumulate.

At first glance, microservices logging may seem simple. You just take the same principles you’ve always followed for monoliths and apply them to each microservice in your application, right? Well, no. The differences between microservices and monolithic architecture amount to much more than a difference in the number of services involved.

Tripwire, created by our friend Gene Kim, is a popular intrusion detection system (IDS) with both commercial and open source offerings. As a fun side project, I put together a Tripwire asset for Sensu. While this is more a prototype than anything else, I wanted to take this opportunity to offer some background on IDS, Tripwire, and integrating intrusion detection into your monitoring workflow, with the overall aim of illustrating how easy it is to deploy solutions with Sensu.

When I listen to our telecommunications customers describe their biggest frustrations, a consistent theme is the need to seamlessly connect across silos, systems, and processes. Work should be intuitive.

Recently InfluxAce Jorge de la Cruz presented on “Modern vSphere Monitoring and Dashboards Using InfluxDB, Telegraf and Grafana”. Jorge is a Systems Engineer at Veeam Software and has been using InfluxDB for years. In case you missed attending the live session, we have shared the recording and the slides for everyone to review and watch at your leisure.

What if you could get your hands on a force multiplier that got rid of the repetitive, routine work that was tying down your team, got more productivity out of your assembled work force, and gave everyone a more challenging, meaningful to-do list that made better use of their knowledge, experience, and passion?

At Grafana Labs, we’re all big fans of the Prometheus and Grafana combination. To an extent, we just won’t shut up about it. We strongly believe in simplicity and think you shouldn’t need any extra effort to understand the metrics of your service(s) holistically. Yet we’ve all been in that situation where it is challenging to fully grasp what the metrics of our service(s) are and what they do. While naming conventions exist, at times they are not followed or enforced.

In part one of this series, I introduced you to Kubeflow, a machine learning platform for teams that need to build machine learning pipelines. In this section, we will learn how to take an existing machine learning project and turn it into a Kubeflow machine learning pipeline, which in turn can be deployed onto Kubernetes. As you are going through this exercise, think about how you can convert your existing machine learning projects into a Kubeflow one.

Today, monolithic applications evolve to be too large to deal with as all the functionalities are placed in a single unit. Many enterprises are tasked with breaking them down into microservices architecture. At LogicMonitor we have a few legacy monolithic services. As business rapidly grew we had to scale up these services, as scaleout was not an option.

There are many challenges that engineering teams face when attempting to incorporate a multi-cloud approach into their infrastructure goals. Kubernetes does a good job of addressing some of these issues, but managing the communication of clusters that span multiple cloud providers in multiple regions can become a daunting task for teams. Often this requires complex VPNs and special firewall rules to multi-cloud cluster communication.