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Migration to Looker

Change tools without copying the mess.

Migration to Looker from Looker Studio, Tableau or Power BI: report inventory by real usage, LookML modelling on BigQuery, reference dashboards rebuilt and reconciled, analyst training. 4 to 8 week engagement, quoted after the inventory.

The problem

A BI migration fails when the 200 existing reports are copied one by one. In most instances, 80% of dashboards have not been opened in three months: we do not migrate them. The engagement starts with the real inventory (who opens what, how often, for which decision), models the surviving metrics once in LookML, rebuilds the reference dashboards and runs them alongside the old tool until the numbers match and the gaps are explained. Analysts are trained on explores so ad-hoc requests stop landing on you. Looker Studio (renamed Data Studio in 2026) can stay for personal exploration: it connects to the same layer.

Who it is for

  • Teams on Looker Studio hitting governance, diverging definitions or row-level access limits
  • Companies on Tableau or Power BI consolidating on Google Cloud who want one semantic layer
  • Scale-ups whose Looker licence is signed but whose LookML project has not started
  • Data leaders who want fewer reports, not relocated ones

What you receive

  • Usage inventory of existing reports (logs, written interviews) and the list of what does not migrate
  • LookML model: views, explores per domain, single measures, drill-downs, role-based access
  • dbt modelling on BigQuery of missing facts and dimensions
  • Five to ten reference dashboards rebuilt in Looker and reconciled number by number with the old tool
  • Documented parallel run, gaps explained and signed off
  • Asynchronous analyst training on explores (videos, exercises, review of their first explores)
  • Decommissioning plan for the old tool

Out of scope

  • Looker licences and negotiation with Google
  • Like-for-like migration of unused reports
  • Ingesting new sources into BigQuery (scoped separately)

Prerequisites

  • Source data already in BigQuery, or an ingestion plan under way
  • A provisioned (or in-progress) Looker instance and a Git repository
  • One business owner per domain, reachable in writing

How it runs

  1. 01

    Weeks 1 and 2: inventory

    Real report usage, metrics to keep, sources, non-migration decisions approved in writing.

  2. 02

    Weeks 3 to 5: modelling

    dbt and LookML, first domain shipped by end of week 3, tested explores.

  3. 03

    Weeks 5 to 7: reference dashboards

    Rebuild, number-by-number reconciliation, parallel run, gaps explained.

  4. 04

    Week 8: cut-over

    Training, documentation, decommissioning plan, two weeks of written questions.

StackLooker · LookML · BigQuery · dbt · Looker Studio / Data Studio (source) · Tableau, Power BI (source)

Frequently asked questions

Why not migrate everything?

Because a report nobody opens costs maintenance and trust. The inventory is written and approved: what does not migrate is archived, not lost.

How long do both tools run in parallel?

Two to four weeks per domain, until the numbers match and the remaining gaps are explained and signed off by the business.

What happens to Looker Studio?

It can stay for personal exploration, connected to the semantic layer. What disappears is the source-of-truth role it should never have had.

How is the quote set?

On the number of domains and reference dashboards. A first quote goes out within 24 hours from your description; it is confirmed after the week 2 inventory. Beyond two domains we split into lots.

Reference implementations

Code you can read before you write to us.

Three complete, public semantic layers built the way we build them for clients: dbt on BigQuery, LookML, dashboard, linter and continuous integration. One per sector.

E-commerce

Margin, average order value, repeat rate, one revenue number. Public thelook_ecommerce dataset, 13 metrics, 3 explores.

View repository Read the guide

Healthcare

30-day readmissions, length of stay, chronic conditions, patient field protection. Synthetic Medicare data in OMOP format, 17 metrics.

View repository Read the guide

Fintech

Payment volume, chargebacks, churn, loan book and delinquency. Generated synthetic data, customer history, 56 metrics, 134 tests.

View repository Read the guide

Guides

8 min read · 5 September 2026

Looker vs Looker Studio: Which One You Need and When to Switch

Looker and Looker Studio solve different problems. Governance, semantic layer, row-level security, pricing, Looker Studio Pro, and when it is time to switch.

Read the guide →

9 min read · 5 September 2026

Migrate from Looker Studio to Looker: When, How, How Much

When to leave Looker Studio for Looker, the migration steps, what to model first, the pitfalls, and realistic timeline and budget ranges for a remote delivery.

Read the guide →

9 min read · 6 September 2026

Looker for e-commerce: margin, AOV, repeat, one revenue number

Ten e-commerce metrics defined once in dbt and exposed in Looker: net revenue, margin, AOV, repeat rate, returns, grain trap. With a public reference repo.

Read the guide →

Describe your context

Three lines are enough: your stack, what is broken, the deadline. Written answer within one business day, call optional.

Request a quote