Live statistics, risk and ML on streaming data, in microseconds, with the same code you use for research.
.dam("100ms").ewma_covariance_matrix(n, 0.94).value_at_risk(&w, 2.33).live()?
Simulated market data
Connect Lightning to market data, message buses or network feeds and process every event as it arrives, as typed Apache Arrow tables.
The complete statistical stack with over 60+ univariate distributions, regression, time series, and incremental updates for EWMA, covariance and regression tests.
Validated research logic runs against the live feed without the rewrite project, removing the divergence between backtest and production.
We often find out what went wrong afterwards.
Lightning analyses it as it happens,
and connects to the systems that can respond.
A common setup combines Spark for batch processing, Flink for streaming and Python for statistics: three APIs, and two clusters to maintain. With Lightning, you can skip the translation and maintenance tax. Write high-level code once, and the same operations behave consistently on arrays, tables and live streams.
let avg_price: Table = history .group_by(&["symbol"]) .select(&["price"]) .mean() .collect()?; avg_price.print(); // one pass over the full dataset
let live: LiveStream<Table> = feed .dam("1s") .group_by(&["symbol"]) .select(&["price"]) .mean() .live()?; for window in live { window.print(); } // a fresh table every second
Close the gap between event and outcome. Separate signal from noise, and act while the window is open.
if window.f64() > var_limit { reduce_exposure(&weights)?; }SimulatedQuant teams often research in Python and maintain a separate production build in C++ or Rust. Lightning runs the same validated pipeline on historical data and live market feeds, removing the translation project and the divergence that comes with it.
Scoring and risk models usually sit across an integration boundary from the payment stream. Lightning runs statistical scoring and your existing machine-learning models inside the live pipeline, leaving more of the clearing window for scenario analysis.
When statistical process control often runs in hourly batches, drift persists until the next cycle. Lightning runs z-scores, multivariate correlation and rolling models on live sensor data and triggers alerts or controls the moment a threshold is crossed.
Pricing, offer selection and ad delivery are time-sensitive decisions. Lightning runs pricing, scoring and personalisation in the live pipeline, using the latest signals across booking flows, digital advertising and in-store pricing.
Sources, windows, statistics and actions run as one process. Typical stages run in tens of microseconds, with latency and provenance visible at every step, and the adaptive engine parallelises CPU effort across intensive stages such as matrix operations.
Predictable under load with no garbage-collector pauses, and many type, memory and concurrency errors rejected at compile time.
A single process with a small, auditable dependency set. Your data stays inside your infrastructure.
Apache Arrow throughout. Data moves to and from Polars, DuckDB, Parquet and your Python models without conversion.
Per-stage throughput, latency and queue depth in Prometheus, with tracing and data provenance for audit.
In-memory, across-the-wire throughput and delivery consistency on a 50 Gbit/s network, timed from request to final verified arrival.
Read →The cost of making five tools behave as one system, and why it never appears on an invoice.
→ For executivesStorage costs compound and compliance exposure expands. When the data lake becomes a governance liability.
→Request a confidential technical walkthrough with the engineer who built it.