Looker: Governed Analytics at Scale
LookML models that scale. Metric dictionary that aligns your organization. Dashboards that are trusted, not questioned.
What we deliver with Looker
LookML model design
Views and explores structured around how the business asks questions, with reusable dimensions and only necessary joins exposed.
Metric dictionary in the model
One agreed definition for revenue, active users, and churn, expressed as LookML so the same number appears everywhere by construction.
Performance and cost
Persistent derived tables placed deliberately, aggregate awareness where it pays, and a review of the explores that scan the most data.
Access and row-level security
Model sets, content access, and row-level filters that reflect real organisational boundaries rather than convenient broad access.
Change control for LookML
LookML in Git with pull requests, validation on every change, and a documented release path instead of edits in production.
How we work with Looker
Looker succeeds or fails on the semantic layer. We start from the questions leadership actually asks, define each metric once in LookML with its owner, and only then build explores and dashboards on top. When a definition changes, it changes in one place and every dashboard follows.
Content sprawl is the other half of the job: two hundred saved looks answering the same question. We consolidate into named, owned dashboards, retire the rest, and set an access pattern that stops the sprawl from returning.
Looker in production: where it breaks
The same handful of problems show up on almost every engagement. Naming them early is cheaper than discovering them at 2am.
Dashboards built outside the model
Custom SQL bypasses governed definitions and numbers diverge again. We migrate one-off logic into LookML where it can be reviewed.
Missing symmetric aggregates
Joins multiply rows and totals inflate. Symmetric aggregates fix it, at the cost of query complexity, and must be designed in.
Persistent derived tables everywhere
Uncontrolled PDTs add build load and stale data. We limit them to cases with a measured benefit and monitor build failures.
When to use Looker
Organisations needing one governed number across finance, product, and ops.
SaaS businesses exposing embedded analytics to their own customers.
Teams that have outgrown spreadsheet definitions but not yet agreed metrics.