arkflow-rs/arkflow

What it solves

ArkFlow is a high-performance stream processing engine designed to handle real-time data processing and intelligent analysis. It bridges the gap between raw data streams (from sources like Kafka or MQTT) and actionable insights by allowing users to apply transformations, SQL queries, and machine learning inference directly on the streaming data.

How it works

Built with Rust and the Tokio async runtime, ArkFlow uses a modular pipeline architecture. Users define a stream in a YAML configuration file, specifying an input source, a processing pipeline (which can include SQL queries, JSON transformations, or Python scripts), and an output destination. It supports various windowing mechanisms (Tumbling, Sliding, and Session windows) to handle backpressure and group data for batch processing.

Who it’s for

It is intended for developers and data engineers who need a low-latency, extensible system for real-time data ingestion, anomaly detection, and complex event processing.

Highlights

• High Performance: Leverages Rust for low latency and high throughput.
• Broad Connectivity: Supports a wide array of inputs (Kafka, MQTT, HTTP, Redis, Modbus, etc.) and outputs.
• sql-based Processing: Allows data filtering and aggregation using standard SQL queries.
• AI Integration: Enables the loading and execution of machine learning models for real-time inference on streaming data.