For the past couple of months I've been working on a data management tool I'm calling OpenDMP. As I've started adding more features, I've run into a scalability issue a bit sooner than I had expected and so I decided to tackle what is hopefully the biggest remaining piece of the project's system architecture.
For the past couple of months I've been working on a data management tool I'm calling OpenDMP. As I've started adding more features, I've run into a scalability issue a bit sooner than I had expected and so I decided to tackle what is hopefully the biggest remaining piece of the project's system architecture.
From the architecture diagram at the top of this post, you can probably tell that the "Processor Service" is where most of the work for OpenDMP gets done.
Dataflows are designed by users in a web app and then handed off to the Processor Service, which sets up Apache Camel routes to ingest, transform, and then store the results. I wanted to allow users to run arbitrary scripts, programs, and/or tools on their data as it traverses their flow, so I created different types of processors users could add to their flows to enable this.
First, the Script Processor, which allows users to enter their own Python or Clojure scripts to transform their data which is then executed within the Processor Service.
Script processor using Python
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AppClues Infotech is a top Mobile App Development Company in USA building high-quality Android, iOS, and Native apps for Startups, SMBs, & Enterprises. Contact us now!
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