All 55 Dependencies
Every package Volga depends on, ranked by repo health score.
Volga targets a specific pain point in modern AI/ML systems (recommendation engines, fraud detection, personalization, search, RAG): features need to be computed consistently across streaming, batch, and request-time contexts, which typically requires stitching together separate systems like Flink for streaming and Spark for batch, plus AI/ML-specific tools like Airbnb's Chronon or OpenMLDB for feature serving.
Volga instead aims to unify these execution modes in one engine, built in Rust on top of Apache DataFusion (query execution) and Apache Arrow (columnar data format), specializing specifically in continuous window aggregations — a common and often awkward-to-implement pattern in feature engineering pipelines.
Apache-2.0 licensed, Volga is an early-stage project (per its own GitHub activity metrics) documented in depth on the project's Substack blog, explaining the rationale as a considered rewrite/rethink of existing streaming and feature-engineering infrastructure rather than an incremental tweak.
