High Performance Streaming Data Processing Implementation and Analysis

High Performance Streaming Data Processing Implementation and Analysis

Introduction

This article is more like a proof of concept of an implementation of microservice development, based on a real world implementation of a specific project build on top of integration of two stock markets, Nasdaq and BIST. The project requires different type of approaches and know-how which can be grouped into two categories, stream data api implementation and data analysis. Both sides have different challenges as they have different requirements.
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Ruby on Rails Development Life-cycle on Docker Containers

Before the containers have been integrated with the applications, the RoR application’s deployments are managed either manually or maintaining more handy tool such as Capistrano. Either way, there are a couple of required procedures needs to be applied on every new change set of the source code wanted to be deployed as a version.

Administration point of view, RoR applications are threated as file based applications, similar to PHP.  Unlike Java or Go, there is not one binary/archived deployable artifact. Therefore, every single change set contains several files and directories which leads the deployment ( directly or indirectly ) a process between SCM and the destination servers. Capistrano handles this quite well, especially In case of any deployment error, there is this automated rollback capability which tries to keep all the target nodes in the cluster on the same version.
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