Sentiment

Introduction As you might know, at the re:Invent AWS recently announced the capabilities of running Lambda by getting the code directly from a docker image you provide, as illustrated here. Moreover, they also announced the Amazon ECR Public and Amazon ECR Public Gallery that you can reach at https://gallery.ecr.aws/. And this was pretty much my reaction: What you might need to know before begin First of all: keep calm, because there’s already a Github Repo to build an entire fleet of microservices - with their respective ECR-based repository - with only one command from the shell. Hopefully, you can get up and running by simply opening a shell, cloning my repo, change the AWS_PROFILE_NAME at the top of the Makefile to point to your profile, and run: ...

Introduction Recently I worked with AWS Lambda and API Gateway to extend my set of personal APIs and collect information from several sources. I wrote an article on that (if you want to have a look). In this article I will talk about the AWS Step Functions service that enable create finite states machines to easy coordinate the components of distributed applications and microservices using visual workflows. Why AWS Step Functions? Because they let me create a tool to gather movie titles in teather, search for reviews about each of them and make a basic sentiment analysis over the review to help me decide what’s worth watching at teather and what’s worth waiting for on Netflix :D More in general, with AWS Step Functions, you can build applications made of individual components that each perform a discrete function: this lets you scale and change applications quickly. Step Functions is a reliable way to coordinate components and step through the functions of your application. They provides a graphical console to arrange and visualize the components of your application as a series of steps. This makes it simple to build and run multistep applications. Step Functions automatically triggers and tracks each step, and retries when there are errors, so your application executes in order and as expected. Step Functions logs the state of each step, so when things do go wrong, you can diagnose and debug problems quickly. ...

Introduction In this article I will talk about my experience with AWS Lambda + API Gateway, GoLang (of course) and Grafana to build a sentiment analysis tool over customizable topics. Who should you read this post? Don’t know, maybe a CIO, a CTO, a CEO, a generic Chief or a MasterChef, for sure an AWS and GoLang fan like me. First of all: to better understand how to use Elasticsearch, read my previous post Elasticsearch over My home Network Attached Storage: it’s not so exciting as it seems, but you will have a general idea about what is Elasticsearch and how can you use it. Second: if you don’t know about AWS Lambda, study it. I personally believe that it represents one of the most interesting services currently offered by AWS: as they state, AWS Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume and there is no charge when your code is not running. The amazing thing is that with a Free Tier trial you have 1 milions requests for free - O.O - to run code of any type of application or backend service - all with zero administration: you just upload your code - unfortunately the online editor for GoLang is not supported yet - and AWS Lambda1 takes care of everything required to run and scale your code with high availability. You can even set up your code to automatically trigger from other AWS services - as I have done with API Gateway - or call it directly from any web or mobile app. And…last but definetly not the least, why I’m writing this post!? Because starting from 15 January 2018, AWS Lambda support GoLang!!! ...