Service

Data Platform

Migrations, pipelines and a warehouse whose correctness is tested: parity checks against the source, typed schemas and reconciliation, with dashboards as support.

Every AI workflow and every model depends on data that someone moved from one system to another. When that move is not verified, the failure is silent: queries return results, and the results are wrong.

The usual symptom is two reports that disagree and an afternoon spent finding out which one is right.

A data platform earns trust when each step proves itself: row counts and totals reconciled to the source, types enforced, and a failed check that stops the run.

What the system does

  • Parity tests

    Source and target compared on counts, keys and value distributions after every load.

  • Typed schemas

    Columns, keys and constraints defined and enforced, not inferred at query time.

  • Loud failures

    A check that fails stops the pipeline and alerts, instead of loading partial data.

  • Scheduled refresh

    Pipelines run on schedule with a visible last-success time.

  • Dashboards as support

    Reporting built on the verified tables, so every number traces to a tested source.

  • Lineage documented

    Each table's sources and transformations written down where your team will find them.

The demo and the case study

Data Platform

Live

Marketplace Data Platform

A migration verified by source-to-target parity tests, and a map of more than 35,000 seller-to-buyer trade routes.

Data
Public: a Brazilian e-commerce marketplace dataset.
Limits
  • A historical dataset; the platform shows the method, not current trade.
  • Analytics on a fixed snapshot, not a live feed.

The method

  1. Inventory

    Sources, owners, volumes, refresh needs and the questions the data must answer.

  2. Migration with parity

    Entity ordering, foreign keys and type conversion handled explicitly; parity tested, not assumed.

  3. Reconciliation

    Totals checked against the source system on every run.

  4. Handover

    Runbooks, tests and alerts your team can operate, or DF1 operates on a plan.

How this is priced

Operate for one pipeline or warehouse; Scale when it feeds several workflows. Migrations and pipelines are quoted as a Build.

Useful add-ons: Data pipeline or migration (quoted); Dashboard.

Plans and prices

Questions about Data Platform

Which databases?

SQL Server and Azure SQL, PostgreSQL, SQLite, cloud warehouses, and files from the systems you already run. The choice follows your stack.

Do you build dashboards?

Yes, as support for a platform or a model, on tables that are tested. DF1 does not sell dashboards on their own.

How do we know the migration is correct?

Parity tests compare source and target on counts, keys and distributions, and the results are part of the handover.

Can our team run it afterwards?

Yes. Runbooks, tests and alerts are delivered with the code. Or DF1 runs it on the Operate plan.

Tell us the workflow. We show you the system.

Describe the process and the data. A reply comes within one business day, from the person who would do the work.