Scaling Analytics
in Financial Technology

Identifying investment trends and opportunities requires the systematic analysis of Securities and Exchange Commission filings from large financial institutions. For one quantitatively focused financial analytics provider, building their business meant scaling up from a manual process to generate analytics for about 100 publicly traded securities to an automated process to analyze more than 3,000.
Case Study

The Challenge

When we got started, our client:

  • Had determined the objectives of the analysis
  • Knew the kind of metrics they wanted to calculate
  • Was stymied by hundreds of millions of records of raw data that was only semi-structured
People Talking

To handle the large number of records, we used Amazon Web Services Redshift, a compressed-column store MPP database that allowed us to realize orders of magnitude better performance over a traditional PostgreSQL implementation at a much lower cost. To handle the semi-structured data, we worked with the client to identify and extract the target metrics and identify cases that needed manual review.

Case Study

The Solution

Enplus designed and implemented the cloud and data architecture on Amazon Web Services. We delivered a custom-built, lightweight workflow library in Python with several command-line utilities for running different jobs on a recurring basis to keep the models up-to-date. Our client receives daily reports as data moves through the system from raw feeds to actionable analytics.