In 2015, we had the ambition to build the most innovative pricing and risk management systems for derivatives, using the latest Big Tech innovations.
We used optimal parallel computing to generate billions of data points and insisted on keeping them all: we did not want to impoverish our data set with slicing or pre-aggregation.
We could not find the right big data tool box so we built our own, around 3 axes:
- High performance numerical data handling (this meant finding the best way to store numbers)
- Fully scalable and cost controlled architecture, with virtually no capacity limit
- Empowering users with data agility: investigate, calculate, simulate, adjust… autonomously!
Banks are big data users; they loved our data module so much that we made it a stand-alone solution and developed dedicated functionalities to enhance the ability for data users to mine value from massive numerical data sets
From there, we have developed an "all in one" high efficiency Big Data solution to handle trillions of data points with flexible functionalities to visualize, aggregate, run calculations, validate and handle data. This solution allows:
- Improved Understanding: aggregate, drill-down and navigate through data points with speed and the flexibility of a pivot table
- Boosted Use: in full autonomy, risk and front office users can simulate, certify, correct, report and calculate using editable Python calculators
- Full Data Set benefit: trillions of points are handled, with no need to pre-aggregate, filter or slice data beforehand, at a fraction of the cost of existing high performance solutions (infrastructure is horizontally scalable)
