Curated articles, resources, tips and trends from the DevOps World.
Most software companies use their own internal network or intranet, where all there computers are connected. Usually, a few among those machines are given access from the internet. But what if it is required to access to a computer on this network that is not reachable from the internet?
Some teams prefer to have a choice of tools for configuring development pipelines, and that is most effective when the tools are integrated and users can retain context as they move between systems for details.
When we talk about Docker, we say that containers are isolated. How do we communicate with our containers, or other applications like MySQL database? It is not useful if we can't access it. Docker has a network concept. It has several network drivers to work with.
Managing and maintaining Kubernetes can be painful when you deploy it on clusters with tens of hundreds of nodes on multiple clouds.
In theory, DevOps is about bridging the gap between developers and ITOps teams. In practice, however, DevOps often can feel as though its real goal is to turn ITOps engineers into developers, without really requiring developers to dive head-first into ITOps work.
Containerization is the new buzz word for developing and deploying apps since they are an efficient way to accelerate development. Container usage has grown exponentially in the last years.
Some AWS resources, such as Lambda or CodeBuild, create their own log groups to CloudWatch Logs as they are being executed, however, they set them up with no log retention keeping all logs forever. This is sub-optimal as the old logs are not really relevant and storing them costs money.
DevOps teams appreciate using DevOps processes, especially in multi- and hybrid cloud infrastructures, for many reasons. For one thing, DevOps breaks down barriers and enables agile software development and continuous delivery of IT operations.
Today (12 July 2019) we had a wonderful technology sharing seminar with the engineering team and machine learning team today in Science Park. Topics involved Kubernetes, Docker, and TensorRT in deploying artificial intelligence service on the cloud.
I’ve been working with Amazon EKS since it became available in eu-west-1. During my time with it, I’ve had some frustrations and surprises. From the offset, it became clear that this managed solution came with trade-offs that required some thought.
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