Blueprint and governance
The LookML project structure, naming rules, Git workflow and the user attributes behind access filters are agreed with the analytics engineering lead and finance before modelling starts.
Platform implementation
Google Looker
We implement Google Looker by modelling your business once in LookML, under version control, so every Explore, dashboard and embedded view queries the warehouse with the same definitions.
Here the semantic layer is code, so most of the effort goes into the LookML project: views, Explores, joins and the measures finance has agreed. Looker queries the database in place and keeps only cached results, so residency follows the warehouse and the region that hosts the Looker instance. Google Cloud has regions in Dammam and Doha, and we confirm which Looker and BigQuery services each one offers before design.
Developers work in Looker's IDE in development mode on a Git branch, and changes reach production through pull requests, LookML validation and content validation. Access comes from model sets, permission sets and access filters tied to user attributes, while LookML data tests check key figures before each deploy.
Google Looker and related marks are trademarks of Google LLC.
How we implement Google Looker
Design, build, integration and run-state support, with each decision recorded for your steering committee.
Embedded analytics
Signed or private embedding in portals and apps, with user attributes passed at sign-in so each customer sees only its own data.
Aggregate awareness and caching
Aggregate tables and datagroups that cut warehouse cost and keep dashboards quick on large BigQuery tables.
Looker Studio alignment
A rule for when teams use Looker Studio and when they use governed Explores, with Looker Studio reading from the LookML model where that makes sense.
Hypercare and managed support
Google updates Looker-hosted instances on a regular release cycle, so we run LookML and content validation against each release and watch query volume, PDT build failures and warehouse spend.
When Looker is the better fit
Looker suits organisations with SQL-literate data teams, a cloud warehouse such as BigQuery, and a need to embed governed analytics in their own products or portals.
- A cloud warehouse already holding the data, ideally BigQuery
- Analytics engineers at ease with SQL and Git
- Agreed definitions for revenue, margin and other core measures
- A clear list of portals or apps that will embed dashboards
Plan Google Looker with Hosn Al Tomoh
Share your current systems, compliance themes and integration map. We will return a phased plan, a risk register and a governance model for your stakeholders to review.