Chris Tozzi

While Kubernetes automates many tasks required to run complex, microservices-based applications, it leaves key tasks up to developers and IT teams.

It sometimes makes sense to treat edge computing not as a generic category but as two distinct types of architectures: cloud edge and device edge.

Managing edge computing workloads is an increasingly compelling use case for Kubernetes.

By distributing data across a CDN network rather than keeping it on a central server, you can typically provide users with faster and more reliable access to the data

Edge infrastructure can integrate with central cloud data centers to deliver performance levels that wouldn’t be possible using the cloud alone.

Rather than fixating on real-time monitoring for its own sake, businesses should focus on understanding and fixing problems in the most efficient manner.

Kubernetes security is supported by some built-in core features, but it must be complemented to fill in gaps that can emerge from a variety of risks.

Data management will increasingly become a real-time endeavor for IT teams, not least because compliance rules are mandating as much.

AIOps is great if you need to remediate relatively simple and straightforward problems that arise in the context of monitoring and management.