# Pascal Data Infrastructure

Source: https://www.trypascal.io/platform/data

Data your whole operation can use.

Turn connected records and files into reusable datasets. Keep their source, quality, freshness, and access rules visible wherever the data is used.

## Capabilities

### Connect the sources you already own

Discover tables, files, streams, and APIs through scoped connections. Establish what each source provides and how it should be refreshed.

### Preserve the original evidence

Keep source inputs available for replay and reconciliation. Transform the data without losing the material it came from.

### Map records to business identity

Normalize source shapes and connect them to canonical business objects, including the relationships downstream work depends on.

### Publish reusable data products

Give a dataset an explicit shape, owner, and quality expectations so other teams can build on a maintained output.

### See freshness, quality, and lineage

Inspect when data arrived, how it changed, and which consumers depend on it. Make incomplete or stale inputs visible.

### Use data across the platform

Query datasets, feed pipelines, supply model inputs, and build object-backed applications under the access policies of the workspace.

## Connected context

Connect sources through Integrations and map records with Ontology. Prepare outputs in Data Pipelines, query them in SQL Warehouse, and use them in model development, applications, Workflows, and agent Tasks.

- Connected sources, original inputs, schemas, and identity mappings
- Published datasets, business object sets, features, and metrics
- Quality checks, freshness, lineage, query history, and exports

## Controls and permissions

Keep the original source behind each derived dataset. Inspect the inputs, transformations, and quality checks before relying on an output. Previews, published datasets, and approved exports each have a separate purpose and review path.

## Example

A lithium readiness check needs dependable telemetry. Data Infrastructure keeps the original ten readings, the reviewed transformation, and the published nine-row snapshot connected. An analyst can see what was excluded and why, while an operator can still find the stale reading that keeps a shipment on hold.

## Workflow

### Keep the original evidence

- Inspect Tacoma Port telemetry, including an invalid timestamp and a separate stale reading. Preparation starts with the observations as received.

### Use an exact published output

- Open the nine-row snapshot produced by reviewed release v2. Its publication and build are available for inspection; the newer blocked draft is a separate version.

### Follow quality and lineage

- Trace the output to the source and the policy that permits one quarantined row. Check freshness and quality before using the data in another decision.

## Next steps

Choose a source and the decision it supports. Define the output, its quality rules, and the people or applications that will use it.

- [Contact sales](https://www.trypascal.io/contact)
- [Pascal Work](https://www.trypascal.io/solutions/pascal-work)