Data modelling specialists for your dedicated team.

Where data modelling fits in a long-term product team.

Grain is the decision everything else follows from. What one row represents, such as an order, an order line or a daily summary, decides which questions the table can answer and which aggregations are correct. A table whose grain is unclear produces double-counted numbers that are very hard to trace back to their cause.

Analytical and transactional models serve different purposes, and each should be designed for its own. A normalised schema is right for transactional correctness and wrong for analysts writing queries; a dimensional model is the reverse. Warehouses that expose the application schema directly push that mismatch onto every analyst, permanently.

What your dedicated team does with data modelling.

Models outlive the systems that produce them. A warehouse model built well remains usable across several source-system replacements, which is why the modelling deserves more design attention than the pipeline moving the data.

That long horizon is the case for the same people holding the model over years, which is what a dedicated development team is for.

Common data modelling use cases.

• Grain stated and enforced Every table with a documented meaning for one row, so aggregations cannot double count.
• An analytical layer separate from the source Models built for the questions people ask.
• Keys that survive source changes Surrogate keys, so replacing a source system does not break every downstream reference.

Adding data modelling skills to your team.

Modelling sits with a dedicated data engineering team, with the model treated as a long-lived asset.

Data modelling specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.