Data Engineering

Every trading system ultimately depends on one thing: market data. Get it late, get it incomplete, or get it wrong, and everything downstream — order routing, risk checks, pricing models, backtesting — is compromised. Yet market data infrastructure is the least glamorous and most under-invested part of most trading platforms. Firms spend millions on execution algorithms and risk models while running market data through a decade-old, single-threaded ticker plant that drops packets when volume spikes. Market data pipelines at trading firms exhibit a consistent pattern: the existing system works at normal volumes, fails silently during market events, and data gaps go undetected until a trade breaks. The fix is not a faster machine. It is an architecture designed for the scale, diversity, and reliability requirements of modern trading. ...

If you are building a financial data platform, the database choice determines what your quants and risk analysts can do — and how fast they can do it. Pick kdb+ and your time-series queries execute in microseconds, but your infrastructure bill runs six figures. Pick ClickHouse and you get analytical power at a fraction of the cost, but you trade off the specialised financial operations language that your quant team has been using for a decade. Pick TimescaleDB and your PostgreSQL-skilled engineers are productive immediately, but you hit query performance walls at petabyte scale. We have deployed all three in production at tier-one banks and hedge funds — kdb+ for real-time market data analytics, ClickHouse for regulatory reporting and risk aggregation, and TimescaleDB for back-office and treasury workloads. Here is what we learned about where each one fits. ...