Build data products that hold up to change.

Data Pipelines. Turn source data into maintained datasets. Preview every transformation, resolve quality issues, and publish a version other teams can depend on.

Design your first pipeline

Illustrative workflow with synthetic data.

Data Pipelines: preview to reviewed output. Pascal product walkthrough. — Product walkthrough
Read the transcript and visual descriptions

Data Pipelines. A clear path from input to output.

Compose the right transformation

Combine visual steps, SQL, and code to filter, join, map, aggregate, or enrich data from governed sources.

Preview before you publish

Inspect sample results at each step. Catch a broken mapping or unexpected join while the pipeline is still a draft.

Define a dependable output

Specify the output schema and quality requirements. Give downstream consumers a clear contract to build against.

Trace changes through the graph

Follow source and field lineage to understand which transformations and published outputs a proposed change will affect.

Run a reviewed version

Separate an editable definition from a published pipeline. Keep each run tied to the version, inputs, and configuration it used.

Operate, investigate, and repair

Inspect failed steps, run history, freshness, and output quality. Use replayable inputs to investigate and rebuild affected results.

Explore Pascal

Catch the problem before it travels.

A malformed sensor timestamp stops a draft. Follow the diagnosis, then inspect the separate reviewed version and its published output.

The Lithium telemetry readiness pipeline connects source readings to typed fields, timestamp checks, and a reusable status output.

Connect the transformation. Native product view with illustrative data.

In practice

An invalid timestamp should not quietly become a readiness signal. The pipeline identifies the bad sensor row, exposes the failed check, and keeps the draft from publishing. Consumers can continue to inspect the output of the separately reviewed version and the policy that produced it.

Example operation.Explore the solution

Data Pipelines in your operation.

Connect it to the rest of your work.

Build from Data Infrastructure sources and make outputs available to SQL Warehouse, applications, and model development. Use Workflows and Automations to coordinate scheduled runs and recovery.

What you work with

Data Pipelines

Pipeline drafts, reusable transformations, and published versions
Source data, SQL, code, tests, and output definitions
Previews, lineage, builds, schedules, and failed runs

Stay in control as the work moves.

Review the pipeline version and its quality rules before publishing an output. A successful preview helps you assess a draft; only a completed build that passes the required checks can publish a new dataset version.

Where you work