# Managing the Complexity of Processing Financial Data at Scale -- an Experience Report

## TL;DR

This paper shares practical insights into managing large-scale, complex financial data processing systems that handle billions of notifications daily, focusing on system architecture, data challenges, and regulatory compliance.

## Contribution

It provides a detailed experience report on handling high-volume, high-velocity financial data at scale, highlighting architectural strategies and operational challenges.

## Key findings

- Systems handle over 1 million notifications/sec during peak times.
- Data complexity includes volume, variety, velocity, and veracity challenges.
- Market events like Brexit significantly stress system performance.

## Abstract

Financial markets are extremely data-driven and regulated. Participants rely on notifications about significant events and background information that meet their requirements regarding timeliness, accuracy, and completeness. As one of Europe's leading providers of financial data and regulatory solutions vwd processes a daily average of 18 billion notifications from 500+ data sources for 30 million symbols. Our large-scale geo-distributed systems handle daily peak rates of 1+ million notifications/sec. In this paper we give practical insights about the different types of complexity we face regarding the data we process, the systems we operate, and the regulatory constraints we must comply with. We describe the volume, variety, velocity, and veracity of the data we process, the infrastructure we operate, and the architecture we apply. We illustrate the load patterns created by trading and how the markets' attention to the Brexit vote and similar events stressed our systems.