Migrate to Looker. Carry What Matters. Leave the Rest Behind.
BI migration is not a copy-paste job. Your dashboards, metrics, and business logic must be rethought and rebuilt for Looker — not just moved. SquareShift does it correctly, in parallel with your live system, with zero data risk.
Tableau to Looker
Domo to Looker
Power BI to Looker
MicroStrategy to Looker
Qlik to Looker
Swiftype to Elastic
250+ BI Migration Completed
6-Source platforms supported
0-Data risk during migration
100% Parallel build - live system stays up
Google Cloud Premier Partner · Looker Delivery-Verified: SquareShift's migration quality has been independently validated by Google Cloud — the highest credential available for Looker partners.
Platforms We Migrate to Looker
Tableau → Looker
The most common migration path for Google Cloud customers. Tableau's calculated fields and LOD expressions don't map directly to LookML — they need to be translated into measures, dimension groups, and derived tables with correct join structures. We audit every workbook before touching the model.
Domo → Looker
Domo's Beast Mode calculations and proprietary connector ecosystem don't translate directly to LookML. Data stored in Domo's cloud may need to be extracted to your warehouse before migration begins. We assess the data location and connector architecture before scoping the migration.
Power BI → Looker
Power BI's DAX measures and Power Query transformations are architecturally different from Looker's semantic model. Complex time intelligence functions and nested DAX require careful rethinking — not direct translation. Power Query transforms are typically rebuilt as dbt models before Looker is connected.
MicroStrategy → Looker
MicroStrategy's object-based report architecture and complex hierarchy definitions require careful deconstruction before LookML can be designed. We map MicroStrategy's metric engine to Looker's semantic layer, preserving complex KPI logic while simplifying the overall model architecture.
Qlik → Looker
Qlik's associative engine and script-based data transformations are fundamentally different from Looker's join-and-explore model. Set analysis logic, QlikView scripts, and associative relationships require a full re-architecture into LookML — a direct port produces unusable output.
Custom BI → Looker
Homegrown dashboards, spreadsheet reporting environments, and custom SQL report pipelines often have no documented data model at all. We start from a warehouse schema audit, design the LookML architecture from scratch, and typically deliver a semantic layer with better coverage than what existed before.
Our Methodology
The SquareShift Migration Process
A five-phase process built on one core principle: your existing platform stays live until Looker is fully validated. No hard cutover. No surprises.
Phase 1
1–2 weeks
Migration Audit & Content Inventory
We begin with a structured audit of your existing BI environment — cataloguing every dashboard, report, data source, and calculation. Critically, we assess usage: dashboards with no active users in 60+ days are flagged for retirement rather than migration. You get a clear picture of actual migration scope — what exists, what's actively used, and what can be decommissioned — before any build work begins.
Phase 2
1–2 weeks
Architecture Mapping & Logic Translation Plan
Before any LookML is written, we design the full target architecture — translating your source tool's logic into the correct Looker constructs. This means mapping calculated fields to LookML measures, identifying which transforms belong in dbt versus LookML, designing the explore and join structure, and specifying how access controls will be reimplemented in the new environment. You review and approve the blueprint before build starts.
Phase 3
4–12 weeks
Parallel Build in Looker
We build the complete Looker environment — LookML model, explores, dashboards, access controls, and (where applicable) Conversational Analytics and Gemini configuration — while your existing BI tool stays fully live. Your teams experience zero disruption. Nothing is cut over until the Looker environment is signed off. The parallel build phase duration depends on the scope identified in the audit.
Phase 4
1–2 weeks
Side-by-Side Validation & UAT
Every key metric in Looker is reconciled against its equivalent in the source tool before any user is moved. Discrepancies are surfaced, diagnosed, and resolved — not pushed to users to discover post-cutover. Your business stakeholders run user acceptance testing on the Looker environment with the source tool still live as the reference point. Cutover only happens after formal sign-off.
Phase 5
Ongoing
Training, Managed Cutover & Post-Migration Support
We train your internal Looker developers on the LookML model and run structured end-user onboarding before the final cutover. The cutover itself is managed — phased by team or by department where needed, never a simultaneous hard switch. After go-live, SquareShift offers an ongoing support retainer covering model evolution, new integrations, performance optimization, and Agentic BI feature rollouts.
Migrations That Delivered
"We had 200+ Tableau workbooks, many of them with years of accumulated calculated fields nobody fully understood. SquareShift audited everything, retired about 40% of it, and delivered a Looker model where our analysts went from a constant backlog to same-day self-service. The side-by-side validation phase was what gave leadership the confidence to cut over."
"We tried migrating from Power BI to Looker internally and got stuck on DAX translation after two months. SquareShift came in, did the architecture mapping in a week, and built the entire LookML model in eight weeks. What took us two months to fail at, they had done and validated in ten weeks total. The dbt layer they designed alongside it made everything cleaner."
Jamie Jackson, Director of Analytics, Global eCommerce Company
Priya Joshi, Head of Data Engineering, Series C SaaS Platform