Operations | Monitoring | ITSM | DevOps | Cloud

Everyone knows it’s been a tough time for businesses. All flights, conferences and in-person meetings have been canceled. The novel coronavirus (COVID-19) has even made us all stand apart from each other and, if anything, bump elbows only. Times are tough. For those of you in the software business, you know you’ve got it easier than some industries. You CAN work from home. You CAN continue developing. And you should, too.

Are you building and deploying software manually and would like to change that? Are you interested in learning about building a Jenkins pipeline and better understand CI/CD and DevOps at the same time? In this first post, we will go over the fundamentals of how to design pipelines and how to implement them in Jenkins. Automation is the key to eliminating manual tasks and to reducing the number of errors while building, testing and deploying software.

In today’s era of microservices, containers, and containerized applications, software architecture is more complex. Kubernetes is king in this environment, orchestrating an army of Docker containers in more distributed environments.

Longhorn is cloud-native distributed block storage for Kubernetes that is easy to deploy and upgrade, 100 percent open source and persistent. Longhorn’s built-in incremental snapshot and backup features keep volume data safe, while its intuitive UI makes scheduling backups of persistent volumes easy to manage. Using Longhorn, you get maximum granularity and control, and can easily create a disaster recovery volume in another Kubernetes cluster and fail over to it in the event of an emergency.

The first means to collect security-relevant information at Cloud SIEM Enterprise (CSE) was our Network Sensor. It was built to analyze network traffic and provide visibility beyond traditional SIEM's down to the network-level. Beyond organizing packets into flows, the sensor supports more advanced features such as decoding of common protocols, file carving, SSL certificate validation, OS fingerprinting, clustered deployment and more.

When I read Greg Ferro’s infamous “Why I hate ITIL so much” blog back in 2015, I have to admit that I agreed with many (albeit not all) of what he said. Maybe it’s the issues that I have with authority in general, or maybe it’s my many years of working within the constraints of ITIL and ITSM in operating systems and services – but I truly believed (and still do) that well-educated, experience and consensus-based pragmatism is what actually gets things done.

Andreessen Horowitz recently published a blog about the Heavy Cloud Costs and Scaling Challenges of The New Business of AI, in which they describe how AI companies are facing cloud cost challenges, which are impacting their margins. As someone who used to manage a fully home-grown on-site distributed speech recognition platform for an industry leader, I know firsthand that ML can be expensive and challenging to maintain. However, it doesn’t have to be.