Building Real-Time Data Pipelines for Financial Services

In capital markets, milliseconds matter. A real-time data pipeline that delivers stale data — even by a few seconds — can mean missed opportunities or significant losses. Here's how we architected a trading intelligence platform that processes over 2 million events per second.

Architecture Overview

The core stack: Apache Kafka for event ingestion, Apache Flink for stream processing, ClickHouse for real-time analytics, and a React-based dashboard for visualisation.

Kafka handles the firehose of market data, order events, and trade confirmations. Flink applies enrichment, aggregation, and anomaly detection logic in real time. ClickHouse's columnar storage enables sub-second queries across billions of rows.

Key Design Decisions

Exactly-once semantics: Financial data cannot be double-counted. We used Flink's checkpointing mechanism combined with idempotent Kafka producers to guarantee exactly-once processing.

Schema evolution: Market data schemas change. We adopted Apache Avro with a schema registry to handle backward-compatible schema changes without downtime.

Backpressure handling: When downstream systems slow down, the pipeline must not lose data. We configured Kafka topic retention to act as a buffer, allowing downstream systems to catch up.

Outcomes

The platform reduced trade reconciliation time from hours to minutes and enabled real-time risk exposure monitoring that previously required overnight batch runs.