Data and databases

Good software needs good data underneath it.

Repair unreliable data, migrate databases, and build reporting that reconciles with the source records.

The problem

The number depends on who you ask.

Two systems disagree. A report takes a week. Nobody trusts the dashboard, so decisions get made on instinct instead.

We fix the layer underneath: schemas, pipelines, and reporting that produce one answer people believe.

Capabilities

What we work on

Database migration

Move platforms without losing history or breaking what reads from it.

ETL and pipelines

Get data where it needs to be, on a schedule you can rely on.

Reporting

Operational dashboards built on numbers that reconcile.

Data cleanup

Deduplicate, normalise, and fix what years of entry has done.

Data APIs

Let other systems read your data without direct database access.

High volume systems

Transactional workloads that stay fast as they grow.

From four answers to one
Four systems, four numbers Nobody trusts the dashboard
Pipelines and modelling ETL, reconciliation, one definition per metric Stork builds
A number people act on Reporting that survives being questioned

The hard part is agreeing what a metric means, not moving the rows.

What we work with

  • MySQL
  • MariaDB
  • SQL Server
  • PostgreSQL
  • ETL
  • Data warehousing
  • Reporting
  • Migrations
How it works

How a data project usually goes

  1. Profile the sourcesInspect schemas, sample records, data volumes, and slow queries to locate quality and performance problems.
  2. Define the rulesAgree record identifiers, metric definitions, and how duplicates or conflicting values should be resolved.
  3. Build the data pathCreate the schemas, transformations, and repeatable migration or ingestion jobs the project needs.
  4. Reconcile the resultsCompare counts, totals, and representative records with the sources, and investigate differences before switching consumers.
  5. Keep it dependableAdd freshness and failure checks, document data definitions, and tune queries against real workloads.

Which report or database can your team no longer trust?

Tell us where the data comes from, what does not reconcile, or which workload has become too slow.