SogFlow

SogFlow is a dataflow-oriented software framework for building, deploying, and monitoring data processing pipelines that can run both batch and streaming workloads. It provides a graph-based programming model in which pipelines are composed of operators connected by data streams. The framework aims to be language-agnostic through adapters and connectors, enabling components written in different languages to participate in a single pipeline.

SogFlow originated at SogTech Labs as an internal data integration tool and was later released as an open-source project under the SogFlow Foundation. Development is community-driven, with contributions from users in finance, technology, and manufacturing sectors. The project has released multiple major versions and continues to evolve with new connectors and operators.

The core consists of the SogFlow Core, a streaming and batch runtime, a DAG-based scheduler, and a stateful operator runtime. A compiled pipeline describes the operations and their data dependencies; at run time, the engine performs scheduling, fault tolerance, and back-pressure management. SogFlow provides a domain-specific language (SogFlow Script) and APIs for Python, Java, and Go, plus a library of built-in operators for transformation, aggregation, windowing, and I/O. Key features include exactly-once processing semantics in streaming mode, checkpointing, autoscaling, and multi-tenant isolation.

Connectors and ecosystem: SogFlow supports connectors for message queues (Kafka, Pulsar), cloud storage (S3, GCS), databases, and file systems, along with streaming and batch data sources. Use cases: data ingestion and ETL pipelines, real-time analytics, anomaly detection, and event-driven processing in IoT and enterprise contexts.

Reception: It is often compared with other dataflow systems such as Flink and Beam; users cite rapid pipeline development and strong ecosystem as advantages, while some note complexity for large-scale deployments.