A rogue algorithm just dumped 50,000 shares. You have 14ms to react.

How event-driven architectures detect algorithmic flash crashes in milliseconds.

On May 6, 2010, the U.S. stock market experienced the “Flash Crash,” wiping out nearly $1 trillion in market value in 36 minutes before rebounding. The culprit was a massive algorithmic sell order that triggered a cascade of automated high-frequency trading (HFT) reactions.

Today, algorithmic trading accounts for the vast majority of equity volume. The business problem is market stability: How can an exchange or an institutional broker detect a rogue algorithm or a cascading market anomaly and trigger a circuit breaker before the damage is irreversible?

The obvious solution is a traditional database. You ingest the trade data, write it to a table, and run an anomaly-detection query every few minutes.

But when trades execute in microseconds, time is the enemy. Waiting to batch-load transactions into a data warehouse like Snowflake or BigQuery defeats the purpose of real-time fraud or crash prevention. By the time the query finishes running, the market has already crashed.