Whether you’re working with a single-instance application or a complex deployment of dozens of orchestrated microservices, it is important to know that the code is working the way it should, and how people and outside systems are interacting with it. I’ve written before about instrumentation of applications and even showed a toy example using amazon X-Ray, but I thought I should devote some space to observability and why it is important.
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I recently received my AWS DeepLens device. I’m by no means a machine learning expert. However, Andy Jassy’s announcement of the device at 2017’s AWS re:Invent implied that the DeepLens would put Machine Learning and Computer Vision in the hands of non-experts and make it easy. So, let’s try out one of AWS’s pre-trained samples to see just how easy this device is to use.
One of the great leveling factors of cloud technology is the ability for small companies and individuals to do things that, not that long ago, would have required the support of a larger IT organization’s infrastructure, and doing them quickly and affordably. One of the most powerful yet simple of these technologies is ad hoc querying of data offered by Amazon Athena.
DevOps as a practice has largely grown from the need to manage infrastructure and configuration for large scale applications. Frequently this has led to the technical choice of fleets of Linux-based application servers operating in a dynamic environment such as VMWare or AWS. The reasons for this technical choice are straightforward. Linux enjoys a lightweight and standardized remote administration mechanism through Bash and SSH. Software installation and dependency management are usually a breeze thanks to Linux distributions’ package managers.