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Innoplexus supports organizations – such as Pharma, Life Sciences, Banks and Financial Services – that need to constantly evaluate and improve their operations to keep up with the fast pace of change, like an ever-changing regulatory environment, disruptive tech start-ups or growing customer expectations. Innoplexus’ Data as a Service and Continuous Analytics as a Service products and solutions are based on proprietary AI for empowered, inspired and faster decisions.
Akshesh Doshi, Software & Data Engineer, Innoplexus
Enter ArangoDB
The most performance-intrinsic, and probably the most exciting, insight provided by kPlexus™ was how top KOLs are connected with each other – that too in real-time! This was initially served by a copy of the data, stored in a graph format, in Neo4j. Due to ArangoDB’s capability of storing graphs together with documents, we were able to avoid storing our data both in a document store and a graph database. To add a cherry to the cake, our benchmarking of ArangoDB’s performance as a graph database, against Neo4j, revealed 4-5x faster performance for few of our complex queries with MMFiles storage
And this is what we finally built:
Moreover, ArangoDB’s full-text feature obviated our need to index data into Elasticsearch for a search feature that we provide. Also, thanks to Jan Steemann whose code snippets from the official cookbook documentation served us as a base code to build further upon.
Adding more, the ability of ArangoDB to perform JOINS via AQL allowed us to model the data in a much cleaner way, eliminating redundancy as much as possible.
All in all, with the help of ArangoDB, we were able to maintain our application with less number of developers and saved on our server infrastructure cost.