PipelineFlows

PipelineFlows is a software framework designed to help developers and data teams construct, execute, and monitor data pipelines. It models pipelines as directed acyclic graphs of interconnected components, including data sources, transformations, and sinks. The framework supports both batch and streaming workloads and emphasizes reliability, scalability, and observability across the pipeline lifecycle.

PipelineFlows comprises a core execution engine, a scheduler, and a runtime environment that manages task parallelism, fault handling, and resource allocation. Pipelines are defined with a declarative description (in YAML or Python), enabling reuse of components, versioned deployments, and parameterization. The system provides a library of built-in operators for common tasks and supports pluggable connectors to databases, file systems, message queues, and cloud services. It offers backpressure handling, checkpointing, and options for exactly-once or at-least-once delivery semantics, depending on the workload requirements.

Key features include pipeline versioning and lineage tracking, observability through metrics and logs, and integration with monitoring and orchestration tools. PipelineFlows can run on on-premises clusters or cloud environments and is designed to work with container orchestration platforms.

Common use cases are ETL/ELT processes, data migration, real-time analytics, data lake ingestion, and feature pipelines for machine learning workflows.

The project is maintained as an open-source initiative with a community-driven governance model and documentation that covers best practices, development guides, and examples.