Data Engine — Collection

Data pipelines with provenance, not black boxes.

Audiences: Data engineering · Analytics · Data science

What this solves

ETL, reporting, and data quality checks at scale need full provenance on every transformation — not scripts that fail silently.

Included connectors

PostgreSQL

Data source and destination

File Storage

CSV and JSON file sources

Example workflow

1. system: Source triggers — Scheduled or event-driven extraction begins.
2. ai: Transform + validate — ETL Pipeline transforms with quality checks at each stage.
3. ai: Report generation — Report Generator produces structured output from verified data.
4. system: Provenance logged — Every transformation step recorded.

Trust boundaries

• Data transformations are logged to the Ledger at each step.
• Streaming anomaly alerts respect org-scoped notification policies.
• Reports cite data sources with timestamps for verification.

Approval model

Data quality failures are flagged with actionable context; schema changes require human review.

Deployment

Governance tags

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