It’s much easier to iterate your platform on containers before deciding to use more “traditional” computing systems in Staging or Production. It’s not a requirement to use Docker or Kubernetes, but even if the system is using containers all the way up to Production, many of the DevOps cycles can be done more quickly because of how quickly environments can be refreshed.
These distributions are also distributed as Docker containers which make them easy to use and build prototypes and then scale in production using Kubernetes. What’s Kubernetes? It’s now become the platform of choice to distribute stateless apps (API, UI, Queue Processors) and recently stateful apps (Databases, indices, Queues) as containers. Since containers are running on the host operating system, it’s a very efficient use of hardware whether it’s physical or virtual.
Docker, like other containerization engines, is now generally compliant with the OCI (Open Container Initiative. As long as they are OCI compliant, images can be run on Docker Swarm, Kubernetes, or DCOS/Mesos. Kubernetes just happens to be the most widely used and has the largest community. A Kubernetes overview by The New stack gives an excellent introduction if you are curious.
These are technologies that make STACK easier.
Multi-Cloud / Hybrid-Cloud / Baremetal / Containers
Deployment can be done on anything that can run an operating system. Using these components that are both open source and commercially supported, we have options to deploy it on any system that we need.
Here’s an architecture that may be used to build a
We’ve talked about the components that can power a global data & analytics platform, in the next part of this series we will design an example that could very well be your next platform design. We’ll even account for a hybrid / multi-cloud design for some of the components.
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