For pharma, biotech, medical devices, CROs, and labs: code-native, validation-friendly ingestion for regulated data across GMP, GCP, and GLP. Built on dlt, run on dltHub.
Life sciences teams building on dltHub
For regulated life sciences · GxP
dlt is code-native, test-first ingestion; dltHub runs it. Together they satisfy CSA and GAMP 5 without freezing versions or exposing you to platform churn.
Pin the validated baseline
git tag validated/veeva-vault@1.4.0tag created · this exact code is what you validate
Prove it behaves the same every run
pytest tests/behavioural/214 passed · same input, same output
Open the evidence trail
dlthub showrun history, schema contracts, lineage · the ALCOA+ record
dlt consolidates data from Veeva Vault, LIMS, REST APIs, SQL databases, and files into Snowflake; dltHub runs it as managed infrastructure in your own environment. The 30-day POC ships with the validation evidence your quality team reviews, and Snowflake stays your orchestrator of record.
One regulated source into Snowflake, in your own environment, time-boxed to 30 days. For example Veeva Vault or a LIMS source via a read-only API. The output is a reusable template, not a bespoke pilot.
The pipeline itself, ready for your quality process.
Generated by the run and mapped to CSA and GAMP 5.
Pipeline two to ten gets cheaper, not equally expensive.
CSA and GAMP 5 favour risk-based, critical-thinking assurance over document volume. With dltHub you validate one artefact, the dlt pipeline pinned to a git tag, and the behavioural test suite is the objective evidence: same input, same output, re-run on every change.
dltHub provides the traceable run records: who ran what version, when, with which schema, and what it produced. Electronic signatures and your quality management workflows stay in your QMS; dltHub supplies the audit trail they reference.
The dltHub context catalog records object-level lineage, schema contracts, and run state for every load. Data stays attributable, contemporaneous, and complete because the record is generated by the run itself, not assembled afterwards.
Yes. Read-only access is enforced in the pipeline code itself, for example via Veeva's read-only Direct Data API, so the control is testable and reviewable rather than a policy statement.
No. The git tag is your validated baseline; you choose when to upgrade. Re-running the behavioural suite against the new version produces the evidence that behaviour is unchanged, so upgrades become a controlled change instead of a revalidation project.
Bring one source and one stakeholder from Quality or Compliance. We map the validation package to your framework before any code runs.