Another KubeCon/CloudNativeCon is upcoming in Europe, this time Paris is the city hosting this big event. Alphons, Ernest, Luuk, RobV and Tom will travel to the city of light to bring back the latest trends and developments. And hopefully we will meet you all there as well! We’re eager to go. And once back we will report our findings on this blog again, and maybe even focus on some subjects in detail.
Looking forward to the conference, there are some important themes to mention:
- Platform engineering
- Observability and eBPF
- AI
# Platform engineering
Last year was platform engineering year, this subject hit every IT conference and we saw customers discussing, using and evaluating this. Still there are lots of developments going on, so it’s good to check out some related talks. Kubernetes seems to be one of the best platforms to adopt this best practice, however this merely depends on the IDP (Internal Developer Platform) you’re using. Is Backstage really to best IDP available? If so, are there any improvements on deploying and managing this? Where is the community heading to? And what alternatives are there? Lots to consider, so be sure to attend BackstageCon of de Platform Engineering Day on Tuesday, or join a few talks of the Platform Engineering track during the rest of the conference.
# Observability and eBPF
eBPF provides a neat way to expose all kinds of data and metrics from your Kubernetes cluster, on a very detailed level, up to processes within your pods. In the world of observability, eBPF is currently one of the hottest themes. Last year we saw lots of possibilities and experimental tools on this. Meanwhile some tools became more mature and are ready to apply on your production workloads, taking away most complexity of eBPF. So what are these tools? Which do best fit to your clusters? And what value do they add to your ecosystem? Join Observability day on Tuesday or one of the observability talks during the conference.
# AI
So… Artificial intelligence remains popular and increases your workloads. More applications are using AI models to perform all kinds of calculations and data analysis. So your clusters need GPU nodes to run these workloads. However, they might have special requirements and you may need to think about centralizing models. How do you handle this? What is the best way to manage this? What tools can help you? And what to expect in the upcoming years on these vast growing workloads? Be sure to attend some ML/AI + Data Processing talks.