Sylvain Kalache
Contributor
Sylvain Kalache is the co-founder of Holberton, an edtech company training digital talent in more than 10 countries. An entrepreneur and software engineer, he has worked in the tech industry for more than a decade. Part of the team that led SlideShare to be acquired by LinkedIn, he has written for CIO and VentureBeat.
More posts by this contributor
6 strategies for running more effective startup board meetings
For companies that use ML, labeled data is the key differentiator
Kubernetes is fast becoming an industry standard, with up to 94% of organizations deploying their services and applications on the container orchestration platform, per a survey. One of the key reasons companies deploy on Kubernetes is standardization, which lets advanced users see productivity gains of up to two times.
Standardizing on Kubernetes gives organizations the ability to deploy any workload, anywhere. But there was a missing piece: the technology assumed that workloads were ephemeral, meaning that only stateless workloads could be safely deployed on Kubernetes. However, the community recently changed the paradigm and brought features such as StatefulSets and Storage Classes, which make using data on Kubernetes possible.
While running stateful workloads on Kubernetes is possible, it is still challenging. In this article, I provide ways to make it happen and why it is worth it.
Do it progressively
Kubernetes is on its way to being as popular as Linux and the de facto way of running any application, anywhere, in a distributed fashion. Using Kubernetes involves learning a lot of technical concepts and vocabulary. For instance, newcomers might struggle with the many Kubernetes logical units such as containers, pods, nodes, …
