ANZ Pied Piper

The cloud data co-location use case is a very interesting one. Potentially the biggest reasons why organizations choose not to move a workload to the cloud include: compliance / regulatory requirements lock-in risks and difficulty/costs of migrating to another public/private cloud unpredictable and/or high costs These reasons also play an important part in the ongoing workload repatriation activity we are seeing in the market. The idea that "cloud is not a destination, it's an operating model" is finally sinking in. But, when we talk about destinations, it doesn't all have to be public or private. There is a third location that plays an interesting part Last year I participated in a project that studied the feasibility of implementing "cloud data co-location". This basically consists on hosting a traditional storage array in a datacenter that is physically close to a public cloud and connect to it through a high-throughput low-latency link. Some ...

Serverless and Function-as-a-Service are not new concepts but they have become very popular since AWS launch Lambda in 2015. The objective of this post is to provide you with some guidance so you can experience it for yourself. So we will do a very quick introduction and explore the pros and cons and jump straight into the hands-on. The idea is that you create a function on your language of choice which is then run on a container when triggered by an event. The word serverless comes from the fact that you only worry about the code, ie you don't have to manage the server, the container, the virtual machine or whatever it is running on. From that point of view PaaS is "serverless" as well, hence Function-as-a-Service is perhaps a better term. The difference between PaaS and FaaS is that with PaaS your unit of deployment is the full microservice (which is likely to contain several functions), whereas in FaaS it is only a function. FaaS leverages the speed at which cont...

Image analysis is one of those services with a very high WOW factor. Even if you take your time to study machine learning, deep learning and you are able to do your bits and pieces in this new brave world, your are still likely to get surprised by the capabilities. The big cloud providers analyze many millions of photos on a daily basis and the bigger the data set becomes the more predictive power it will gain. At Pied Piper we will be mostly using these and other readily available capabilities for our team projects as opposed to build our own models. And of course we can do all this from the comfort of Python which we can easily integrate with other elements like Cloud Native Applications and Internet of Things for whole end-to-end solution. AWS rekognition offers the following services: Object and scene detection. It provides a confidence score for each object it detects Facial analysis. Locates faces in an image and provides face attributes and emotions Face comparison. It ...

In the Pied Piper program we run through a Vagrant tutorial as we found it a great tool to help set up other labs. We are not going to cover Vagrant in detail in this blog as there are plenty of good examples out there. If you are interested in the exercises we did you can visit the repository: https://github.com/cermegno/vagrant-lesson However, something I felt is not sufficiently document and would make a good candidate for a post is how to use Vagrant to deploy to AWS and particularly from Windows. I found a couple of posts that provided good detail but none of them prevented me from running into Windows specific issues. If you are running it from Linux, this article will still help you as I will point out the differences anyway. Install prerequisites Firstly, as the Vagrant documentation states, "providers" other than VirtualBox require a plug in. This is how you install the AWS provider: vagrant plugin install vagrant-aws Deploying to AWS EC2 generally mea...

"Polly" is an ingeniously named AWS service that allows you get convert text to a sound file. It is part of the AI portfolio. Another example that we will see in another post is "Rekognition". Long time ago if you wanted to do these kind of tasks you would have to build your own models with lots of samples, train them, improve them and so on. It is impossible to cater for every AI use case in this fashion but the ones related to human senses such as speech, hearing and vision are being already offered by most cloud providers today. These services are conveniently accessed by an API Although the service has the name of a parrot it provides multiple voices with different accents. Each voice has a different person name, ex: "Joanna". Surprisingly it even includes some children voices in some languages. It would be awesome if they also offered a good strong Irish accent ... it would make a good addition to a joke-telling app running in a smart beer fridge...