data-pipeline-manager

What problem does it solve?

Data pipelines in research and production environments are prone to validation gaps, unhandled errors, and brittle recoverability, leading to downtime and inconsistent results.

Core Features & Use Cases

• Six-stage workflow design, including design, input validation, transform, output validation, error handling, and monitoring.
• Robust error handling patterns: retries with backoff, checkpointing, structured logging, and recovery from partial failures.
• Monitoring and observability across stages with dashboards and alerting; supports bioinformatics and data processing pipelines.

Quick Start

Define a blueprint describing the six-stage workflow and enable checkpointing. Then configure on your orchestrator to run a dry-run on a small dataset to observe progress and recovery behavior.