LookML specialists for your dedicated team.

Where LookML fits in a long-term product team.

Defining metrics in version-controlled files is the property that makes LookML valuable. A metric change goes through a pull request with a reviewer and a history, which is the standard software practice that BI tools have generally lacked entirely.

It also means every user query is generated from the same model, so a metric cannot be calculated two ways. That consistency is the point, and it is what a tool allowing arbitrary SQL against raw tables cannot offer regardless of its documentation.

What your dedicated team does with LookML.

The model needs ownership. Without that care, LookML builds up the same debt as any codebase, such as duplicated dimensions, unused views and joins added for one request and never removed, and the governance benefit erodes.

Treating it as a maintained codebase is the practice that keeps its value, handled by a dedicated data engineering team.

Common LookML use cases.

• Metric changes with review and history A definition change as a pull request.
• One calculation across every user Queries generated from the model, so the same metric cannot differ by author.
• The model maintained as code Refactoring and removal, so governance does not erode into sprawl.

Adding LookML skills to your team.

LookML work sits with data specialists, treated as a maintained codebase.

You can add LookML capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.