Data your whole operation can use.

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

Plan your first data product

Illustrative workflow with synthetic data.

Data: source to released output. Pascal product walkthrough. — Product walkthrough
Read the transcript and visual descriptions

Data Infrastructure. Every source. A useful destination.

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.

Explore Pascal

Trust the output. Trace the source.

Ten sensor readings become nine usable output rows under a reviewed quality policy. The malformed reading stays visible in the source.

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

Keep the original evidence. Native product view with illustrative data.

In practice

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.

Example operation.Explore the solution

Data Infrastructure in your operation.

Connect it to the rest of your work.

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.

What you work with

Data Infrastructure

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

Stay in control as the work moves.

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.

Where you work