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!!!
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