Case Study: Deploying RisingWave for Real-Time Stream Processing in a Large-Scale Quantitative Hedge Fund

Company Background

XX Private Fund (name anonymized) is a quantitative hedge fund managing over $10 billion, with roughly 100 staff members, 70% of whom are researchers and traders from top universities. The firm originally focused on high‑frequency futures trading and now needs to process massive daily transaction data with ultra‑low latency.

Challenges with Existing Solutions

Initially the team used ksqlDB for real‑time alerts, then migrated to Flink for more complex ETL. While Flink met functional requirements, it imposed high learning and operational costs on non‑JVM developers, consumed scarce colocation resources, and required extensive monitoring and support.

Why RisingWave Was Chosen

RisingWave’s modern streaming architecture, built on PostgreSQL compatibility, offered a declarative SQL interface that dramatically reduced learning curves and development effort. Its low‑latency performance (5‑10 ms end‑to‑end for table widening and position aggregation, sub‑100 ms for complex queries) matched the fund’s strict latency demands.

Rich source and sink connectors (Kafka, OLTP/OLAP databases, data lakes) and support for JSON, Avro, and Protobuf made data integration straightforward. The platform provides over 50 tables, materialized views, and advanced operators such as temporal joins, interval joins, top‑N, window functions, and watermarks, enabling complex real‑time analytics.

Observability is strong: Grafana dashboards expose fine‑grained metrics, and internal state can be queried via SQL, simplifying debugging. The RisingWave team delivered fast, high‑quality technical support throughout the PoC phase.

Implementation Details

The fund used RisingWave to ingest real‑time trade streams, widen them with dimension tables, and sink results to downstream systems. Specific workloads include:

Real‑time trade stream ingestion, dimension‑based widening, and downstream sinking.

Incremental calculation of account positions and sinking to monitoring systems.

Real‑time profit‑and‑loss (P&L) computation and sinking.

Aggregation of multiple event streams into derived messages for downstream consumption.

Latency tests showed 5‑10 ms for table widening and position aggregation, and sub‑100 ms for P&L and other metrics.

Observability and Support

RisingWave provides Grafana dashboards for metric monitoring and allows SQL queries over internal state, greatly simplifying debugging. The vendor’s professional and responsive support helped the team quickly master the platform and move to production.

Conclusion and Outlook

After several months of deep learning and practice, RisingWave is now running stably in production, delivering low latency, low learning and operational costs, rich functionality, and strong observability. The platform has resolved the fund’s previous pain points and expands the boundaries of stream computing for future complex, innovative trading scenarios.