How do you transition from other bi tools (tableau, power bi) to looker?
- SquareShift Content Team
- 22 hours ago
- 4 min read
For organizations already committed to modernizing their business intelligence platforms, the question is not whether to migrate but how to do so effectively.
A transition from Tableau to Looker or Power BI to Looker requires more than replicating dashboards. It is about safeguarding business knowledge, validating accuracy, and preparing users for a new analytical model.
The following steps outline how migration programs can be approached methodically, based on patterns we have seen across large-scale projects.
Table of Contents
What to Check First Before Migration Begins
How to Preserve Business Logic
What Rebuilding in LookML Involves
How Dashboards Are Recreated in Looker
What Challenges to Expect
How to Secure Long-Term Value
Conclusion: Building Confidence in the Transition
What to Check First Before Migration Begins
Most migration delays occur because foundational issues are uncovered late. Before moving a single dashboard, teams should review their current environment in detail.
Hidden extracts and caches. Tableau dashboards often use extracts that diverge from the warehouse, while Power BI relies on cached datasets that become outdated.
Blended data sources. Reports frequently combine sources through undocumented joins, which cannot be carried forward cleanly.
Irregular refresh cycles. Data pipelines may update at different times, producing mismatched results across dashboards.
SquareShift’s Assessment Accelerator catalogs these dependencies automatically. It scans existing environments, highlights non-warehouse sources, and maps refresh schedules. This provides a realistic view of complexity and prevents early missteps.
How to Preserve Business Logic
The greater challenge is not the dashboards themselves but the business rules buried within them. Tableau’s level-of-detail expressions and Power BI’s DAX measures often encode definitions that are undocumented. Over time, the same metric is created in multiple variations, leading to inconsistencies.
If not addressed, migration risks losing critical knowledge.
SquareShift’s Migration Accelerator identifies duplicate calculations across dashboards and consolidates them into governed LookML measures. For example, five different definitions of customer churn can be standardized into a single reusable metric. This preserves institutional knowledge while eliminating fragmentation.
What Rebuilding in LookML Involves
LookML provides the structure to define and reuse metrics, but it also introduces differences that must be understood.
Relative dates must be explicitly configured, where Tableau defaults can otherwise shift results.
Nested aggregations may require derived tables or aggregate-aware strategies.
Filter and parameter behavior differs and must be redefined carefully.
At SquareShift, validation begins at the View and Explore levels. Automated queries compare LookML outputs with Tableau or Power BI equivalents, confirming that definitions align before dashboards are constructed. This reduces the risk of late-stage discrepancies.
How Dashboards Are Recreated from Tableau, Powerbi to Looker
Dashboards attract the most attention from users, yet this is also where efficiency gains can be achieved. Many environments contain large numbers of unused or duplicated dashboards. Rebuilding these provides little value.
Our migration work typically reveals that 30 to 40 percent of dashboards are inactive yet still maintained.
The Assessment Accelerator helps prioritize high-value dashboards for migration and deprecate those that are redundant.
Direct translations such as bar or line charts can be rebuilt with minimal effort.
Complex visuals like dual-axis charts or treemaps require redesign or the use of Looker plugins.
Validation is performed side by side, comparing Tableau or Power BI dashboards against their Looker equivalents for both numbers and interactivity.
Read more about :Recreating Tableau Dashboards in Looker
Our work has shown that challenges with dashboard consolidation and stakeholder visibility often surface at scale. In one engagement with a semiconductor manufacturer, Looker and BigQuery were used to centralize thousands of siloed reports. Reporting turnaround time dropped by 80 percent, and partners gained real-time visibility into training and sales performance, which had not been possible with the previous setup.
What Challenges to Expect
Every migration encounters obstacles. The most frequent include:
Logic translation gaps. Certain Tableau sets or DAX formulas do not translate directly to LookML. These are either automated by our accelerators or flagged for manual review.
Visualization differences. Specialized charts, floating layouts, and custom formatting in Tableau or Power BI require redesign rather than replication.
Data source complexity. Multi-source dashboards must be restructured into LookML explores, often supported by derived tables.
SquareShift’s Assessment Accelerator provides visibility upfront, highlighting unsupported elements and generating complexity scores. This allows organizations to plan resources and timelines realistically.
Read more about: Common migration in tableau to looker migration.
In practice, reporting delays often erode user trust as much as calculation errors do. An EdTech client we worked with experienced this problem when nightly batch processes left dashboards consistently out of sync with live web activity. By implementing a Looker architecture with serverless pipelines on GCP, reporting became near real-time, aligning dashboards with user activity and restoring confidence in analytics
How to Secure Long-Term Value
The aim of migration is not to replicate the past but to establish a sustainable operating model. With LookML, metric definitions become centralized, version-controlled, and auditable. Workflows move from workbook maintenance to collaborative development.
Organizations typically see reductions in maintenance overhead, fewer disputes over metric definitions, and faster onboarding of new reports. By aligning analytics with cloud-native warehouses, they also position themselves for scale.
SquareShift’s migration programs emphasize this long-term perspective. Automating 70 to 80 percent of conversion work frees teams to focus on redesigning where needed and planning for growth.
Conclusion: Building Confidence in the Transition
A transition from Tableau or Power BI to Looker requires methodical preparation. The steps are consistent: establish reliable data foundations, capture and consolidate business logic, validate models before dashboards, prioritize valuable content, support user adoption, and plan for known challenges.
With SquareShift’s Assessment and Migration Accelerators, organizations gain automation, visibility, and validation frameworks that reduce risk and shorten timelines. The result is not only a successful migration but also a stronger foundation for governed, scalable analytics.
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