Deephaven
The best way to work with live data at scale.
Deephaven is a real-time query engine built around "live dataframes" — a column-oriented, incremental data processing abstraction that updates based on deltas rather than micro-batching. It supports a wide range of data sources including Kafka, Parquet, Iceberg, and custom feeds, and exposes polyglot APIs in Python, Java, Go, JavaScript, and more. The platform is used primarily by large financial institutions for demanding streaming analytics, data apps, and ETL pipelines.
Deephaven builds a directed acyclic graph (DAG) for each query and propagates only data changes (deltas) through the graph incrementally, enabling millisecond-latency results even under high throughput.
Data engineers and quant/finance teams building data-intensive real-time applications at scale
Background.
- Status
- launched
- Business model
- freemium
- Company
- Deephaven Data Labs
Similar projects.
Editorial take on the space this project sits in — momentum signals, adjacent moves, our call on whether the wedge is real. Get pinged when we publish a new read or when the landscape shifts.
Have a take on this space?
Tell us what you’d build differently, where you think the incumbents miss, or what we’ve gotten wrong about this project. Comments + reactions are coming soon.