Choose Your Runtime
Two options for processing rules at scale, depending on your infrastructure.
PySpark
For Spark clusters
Native PySpark integration for evaluating rules across distributed datasets. Works with Databricks, EMR, Dataproc, or self-managed Spark clusters.
- Distributed processing across workers
- DataFrame API integration
- Works with existing Spark jobs
- Python-native development
Rust SDK
For custom pipelines
Maximum performance with our native Rust engine. Build custom data pipelines or integrate with existing Rust applications for throughput-critical workloads.
- Millions of evaluations per second
- Zero-copy memory management
- Multi-threaded processing
- Minimal resource footprint
Performance at Scale
Built for throughput-critical workloads.
Evaluations per second (Rust)
Average evaluation latency
Faster than interpreted engines
Use Cases
Common patterns for big data rule evaluation.
Batch Processing
Process millions of records through your business rules. Ideal for nightly batch jobs, data migrations, or bulk calculations.
Analytics Pipelines
Apply business rules as part of your ETL pipelines. Classify, score, or transform data at scale before loading into your warehouse.
Streaming Data
Integrate with Spark Streaming for real-time rule evaluation on data streams. Process events as they arrive.
Backfill Operations
When rules change, easily backfill historical data. Re-evaluate past records with updated business logic.