Warp

Warp is a high-performance data streaming and transformation platform designed for real-time ETL, stream processing, and multi-cloud data movement. Warp differentiates itself through its performance-optimized runtime and unified batch/streaming API.

Core Product: Warp Stream

Warp Stream is a managed stream processing service that combines the simplicity of SQL with the power of real-time streaming. It runs on a custom Rust-based runtime for maximum throughput and low latency.

Key Features

• Unified batch/streaming - Same SQL and pipeline definitions work for both batch and streaming modes
• Exactly-once processing - Built on a lightweight checkpointing system that tracks offsets and state
• Auto-scaling - Pipeline parallelism adjusts based on data volume and lag
• Multi-cloud - Deploy pipelines across AWS, GCP, and Azure from a single control plane
• Schema evolution - Automatic schema detection and evolution with support for Avro, Protobuf, and JSON Schema

Architecture

Warp's architecture is built around a dataflow model where pipelines are compiled into execution graphs:

• Control Plane - Manages pipeline definitions, deployments, and monitoring dashboards
• Data Plane - Distributed workers running the Rust runtime, connected via a lightweight shuffle network
• Connector Layer - Pluggable source/sink connectors with automatic offset management
• State Store - RocksDB-backed stateful operators with periodic snapshots to S3

SQL Pipeline Example

-- Warp SQL: real-time order enrichment CREATE STREAM enriched_orders AS SELECT o.order_id, o.customer_id, c.name AS customer_name, o.product_id, p.name AS product_name, o.quantity, o.unit_price, o.quantity * o.unit_price AS total_amount, o.order_timestamp, CURRENT_TIMESTAMP AS processed_at FROM orders_stream o JOIN customer_dimension c ON o.customer_id = c.customer_id FOR SYSTEM_TIME AS OF o.order_timestamp JOIN product_dimension p ON o.product_id = p.product_id FOR SYSTEM_TIME AS OF o.order_timestamp WHERE o.status = 'confirmed'; -- Windowed aggregation for monitoring CREATE STREAM minute_metrics AS SELECT TUMBLE(order_timestamp, INTERVAL '1' MINUTE) AS window, COUNT(*) AS order_count, SUM(total_amount) AS revenue, AVG(total_amount) AS avg_order_value FROM enriched_orders GROUP BY TUMBLE(order_timestamp, INTERVAL '1' MINUTE);

Case Study: Real-Time Fraud Detection

A payment processor migrated from a batch-based fraud system to Warp for real-time detection:

• Reduced detection latency from 15 minutes to under 2 seconds
• Processed 50,000 transactions per second per pipeline
• 99.99% uptime over 6 months in production
• 50% reduction in infrastructure cost vs Apache Flink

Comparison with Flink

Deployment Options

• Warp Cloud - Fully managed SaaS with auto-scaling
• Warp Self-Hosted - Deploy on Kubernetes using the Warp Operator
• Warp Edge - Lightweight runtime for IoT and edge deployments

Resources

• Warp Documentation
• Warp Engineering Blog