haipipe-data

A four-stage data pipeline orchestrates data across Source, Record, Case, and AIData stages to transform raw inputs into AI-ready datasets. It enables per-stage loading, cooking, and validation to ensure reproducibility and governance across the entire data workflow.

You orchestrate an end-to-end data pipeline by applying per-stage operations such as load, cook, design-chef, and design-kitchen across the Source, Record, Case, and AIData stages. This manages data transformation from raw sources directly into validated AI-ready datasets.

Yes, you can audit and debug data pipeline configurations by leveraging YAML @ reference docs and _WorkSpace asset stores. These mechanisms ensure reproducibility and governance, allowing you to review and explain pipeline states and per-stage assets effectively.

Yes, haipipe-data supports discovering available Fns, assets, vocabularies, and asset manifests within data analytics environments. This capability allows you to identify and manage end-to-end workflows and inspect per-stage assets and checks.

The best way to validate and review data across multiple pipeline stages is by applying per-stage review and explain operations. This approach inspects and modifies pipelines at each phase, ensuring raw source data is properly cooked and validated into AIData.

You onboard new cohorts by utilizing the dashboard operation within the unified data pipeline to explore all four stages. This provides visibility into per-stage assets and configurations, helping new users understand core concepts and workflow states.