ProductsData
Data Engineer
Build reliable data workflows.
Profile sources, validate quality, and plan pipelines you can actually run.
Who it is for: Data engineers working on quality, schema, and pipelines.
- Connect data
- Inspect schema
- Profile quality
- Plan a pipeline
- Validate
- Observe
Operating model · INPUT → UNDERSTAND → CLEAN → ANALYZE → MODEL → DECIDE → DELIVER
What it solves
- See schema, quality, and readiness before a pipeline is trusted.
- Plan transforms and refuse a fake ‘pipeline succeeded’ state.
Who it is for
- Data engineers working on quality, schema, and pipelines.
What you provide
- Datasets
- An engineering requirement
Requirement
- A job statement — what you need this product to do with the input you provide.
What ORINEL does
- Profiling
- Schema inspection
- Pipeline planning surfaces
- Quality views
How it works
- Connect data
- Inspect schema
- Profile quality
- Plan a pipeline
- Validate
- Observe
What you receive
- Schema and quality views
- Pipeline plans
- Observability from real runs
Examples
- Is this extract pipeline-ready?
- Where are nulls and type breaks before we load production?
AUTO mode
- Describe the source, destination, and requirement. ORINEL plans the pipeline.
EXPERT mode
- Pipeline execution, SQL, and connectors appear only when those backends are provisioned.
Implemented capabilities
- Profiling and dataset management
- Pipeline planning surfaces
- 503/not_provisioned instead of a fake DAG run
Security and trust
- One ORINEL account and the existing authentication system.
- Workspace routes require a signed-in session.
- Safe post-login redirects stay on this origin.
Limitations
A pipeline run against an unprovisioned production schema is gated as not_provisioned. DAG completion is never faked.
One ORINEL account. After sign-in you land in this product workspace, not a different website.