Modernization path

Arbitrate between Power BI and Apache Superset through a tooled comparative audit.

Q: Why migrate Power BI to Superset?

Three legitimate reasons. Technological sovereignty (European public bodies, defense, healthcare). Premium license cost on large estates (10,000+ users). Strict on-premise deployment (regulated zones, ultra-sensitive data). Outside these cases, Power BI typically remains the right choice.

Q: Is Apache Superset really at Power BI's level?

On visualization features, Superset is at the level (charts, dashboards, filters, drill-down). On semantic model, Power BI has the advantage (DAX, reusable measures) — Superset compensates via dbt and SQL. On usage simplicity, Power BI remains more polished. On massive performance (100M+ rows), Superset + ClickHouse can surpass Power BI.

Q: How much does a Power BI → Superset migration cost?

Cost depends on the number of dashboards actually in use, on whether the semantic layer is centralized (a shared model migrates far better than a multitude of local ones), and on the number of business domains involved. Moving to Superset removes licenses but shifts cost toward platform operations: that item is priced separately. Free initial scoping call, 30 minutes to 2 hours.

Comparative audit Power BI vs Apache Superset on three structural criteria: technology sovereignty, Premium license cost, on-premise deployment constraints. Documented Go/No-Go decision, and if migration: semantic model rebuild, equivalent Superset dashboards, SQL-based access governance.

Key figures

3 criteria

discriminating factors: sovereignty, 10k+ user Premium cost, strict on-premise — otherwise Power BI typically the better choice

100M+ rows

threshold where Superset + ClickHouse can surpass Power BI VertiPaq

6-12 months

full migration duration after a Go comparative audit

ATLAS methodology applied

From scoping to deployment, five structured phases.

Each phase aggregates one or more of the ten ATLAS steps. No phase starts until the previous one has delivered its validated artefact. The ATLAS methodology makes AS/400 migration predictable and auditable.

01Phase 1
Comparative audit (3 weeks)
ATLAS steps
E1 Intake · E2 Discovery · E2b Functional spec
Priority phase before any migration commitment. Analysis of existing Power BI estate (dashboard count, models, DAX measures, RLS, Premium capacities). Real usage measurement (Power BI Activity Logs). Evaluation of 3 structural conditions: sovereignty, Premium license cost, strict on-premise. Power BI Premium vs Superset self-hosted vs Preset Cloud 3-year TCO comparison. Signed Go/No-Go decision deliverable.
Phase deliverable
Comparative audit + signed Go/No-Go decision + migration scoping if Go
02Phase 2
Migration scoping & target architecture
ATLAS steps
E3 Dependency mapping · E4 Target architecture
If Go: target selection (Superset self-hosted, Preset Cloud, or Metabase). Analytical database choice per volume (PostgreSQL < 10M rows, ClickHouse > 100M, Trino for multi-sources, DuckDB for ad hoc). RLS strategy (SQL views or Superset RLS). Model layer design (dbt recommended for governance as code).
Phase deliverable
Signed target architecture + RLS strategy + dbt plan
03Phase 3
Model rebuild & business workshops
ATLAS steps
E2e Capture · E4b Pre-migration tests
Model rebuild on database side: centralized SQL views, materialized tables for performance, dbt macros for reusable measures (DAX equivalent). 30 min per critical dashboard business workshops to redefine real need — no pixel-perfect attempt. Parity test suite preparation.
Phase deliverable
SQL/dbt model + validated business workshops + test suite
04Phase 4
Dashboard migration & RLS
ATLAS steps
E5 Migration · E6 Parity validation
Per-dashboard reconstruction in Superset with equivalent widgets (charts, filters, cross-filters drill-down). RLS configuration: filters in SQL views by user authenticated via Superset RBAC, or Superset RLS configured per dataset. Visual parity and security per user role tests. Performance tests.
Phase deliverable
Superset dashboards + validated RLS + signed parity tests
05Phase 5
Cutover & decommissioning
ATLAS steps
E7 Delivery
Progressive cutover user by user or department by department. Power BI ↔ Superset coexistence for 2-3 months (depending on criticality). Dated Power BI Premium decommissioning with savings measurement. End user and editor training. Ops handover to client team with documentation.
Phase deliverable
Superset platform in production + Power BI decommissioned + measured savings + autonomous client team

See the full ATLAS methodology →

Who is concerned

Business context and modernization stakes.

When to leave Power BI?

Three signals push to migrate Power BI to Superset. Technological sovereignty: European public bodies, defense sector, healthcare sector wanting to avoid Microsoft dependency (US cloud, telemetry, restrictive audits). Premium license cost: for organizations with 10,000+ users, Power BI Premium becomes expensive (capacities, per-user). Strict on-premise deployment: some organizations cannot use public cloud (regulated zones, ultra-sensitive data). Outside these three cases, Power BI typically remains more performant and better tooled than Superset for most organizations.

Apache Superset: strengths and limits

Apache Superset (Apache Software Foundation incubated, ex-Airbnb) is the main open source alternative to Power BI / Tableau. Strengths: 100% open source (Apache 2.0), deployable on-premise or cloud, native SQL connectors (PostgreSQL, MySQL, ClickHouse, Trino, Druid, BigQuery, Snowflake), modern interface, granular RBAC, Preset certified (managed Superset). Limits: no central semantic model (each dashboard has its SQL), no DAX equivalent (no cross-functional reusable measures), admin interface less polished than Power BI, editor learning curve. Suited to data-driven organizations with SQL teams.

Migration strategy

Power BI → Superset migration is not a direct translation. Power BI uses a VertiPaq tabular model + DAX, Superset uses direct SQL on databases. Migration requires: data model reconstruction on database side (SQL views, materialized tables, ClickHouse for performance), dashboard rewriting (Power BI visuals don't transpose, but uses are reproducible), security adaptation (Power BI RLS → row-level security via SQL or Superset RLS). See also the Power BI migration path for inverse patterns.

Source platform

Power BI Premium with costly licenses or sovereignty requirements

Power BI↗

Target technology

Apache Superset + PostgreSQL / ClickHouse / Trino, deployable on-premise

PostgreSQL↗Superset↗

Technology alternatives

Compare target trajectories.

Apache Superset (self-hosted) + ClickHouse / Trino

Maximum sovereignty, on-premise, tech-savvy data team. Mastered 100% open source stack.

Preset Cloud (managed Superset)

EU / Canada sovereignty acceptable, team without Superset infra expertise, need for pro support. Preset hosted in the chosen region.

Metabase Cloud or Self-hosted

Simpler alternative to Superset for less technical teams. Less powerful on advanced SQL but smoother learning curve.

Keep Power BI but optimize

If motivation is only cost, optimizing Power BI capacities (embedded vs Premium, P-SKU vs A-SKU, inactive Pro license audit) costs less than a complete migration.

Scoping reference

Typical duration and team for this path.

A Power BI → Superset migration is typically structured over 6 to 12 months depending on volume. For an estate of 50-150 dashboards with centralized database, plan 6 to 9 months with a 4-5 person cell: data architect, Superset/Python developer, two SQL/data engineering developers, business referent. For larger programs (500+ dashboards, multi-domain), plan 9 to 12 months and 6-8 people. Migration is more expensive than incoming Power BI migration because Superset demands more SQL on database side.

Challenges

Reproduce Power BI models on an open source stack without a central semantic model.
Ensure visual and functional parity of dashboards.
Manage access governance and RLS without Power Platform.
Choose the appropriate analytical database (PostgreSQL, ClickHouse, Trino) according to volumes.

ATLAS approach

Audit of Power BI dashboards and actual usage.
Design of the target Superset stack + suitable analytical database.
Dashboard-by-dashboard migration with audited parity.
Rebuild of the model on the database side via SQL views or dbt.

Expected outcomes

Operational, sovereign Superset platform, on-premise deployable.
Access governance via Superset RBAC and SQL row-level security.
Data model reconstructed on database side with SQL views or dbt.
Documented Microsoft Premium license savings.

Identified pitfalls and ATLAS response

What we learned on this migration path.

Pitfall 01

Underestimating the loss of semantic model. Power BI has a centralized tabular model with reusable DAX measures. Superset has no equivalent — each dashboard does its SQL. Naive migration duplicates business logic.

ATLAS response

Model reconstruction on database side: centralized SQL views (PostgreSQL, ClickHouse), materialized tables for performance, or use of dbt to govern the data model as code. Reusable DAX measures become named SQL views or dbt macros. Greater initial effort but maintainable over time.

Pitfall 02

Migrating dashboards identically. Power BI visuals have their specifics (slicers, drill-through, native interactivity). Reproducing pixel-perfect in Superset frustrates users.

ATLAS response

Targeted redesign per dashboard: for each critical dashboard, 30 min workshop with business to redefine real need, then reconstruction in Superset with equivalent widgets (charts, filters, drill-down dashboards via cross-filters). No pixel-perfect attempt.

Pitfall 03

Underestimating performance on large volumes. Power BI VertiPaq compresses in memory and is ultra-fast even on 100M+ rows. Superset queries the database directly — on standard PostgreSQL, can be slow.

ATLAS response

Adapted analytical database choice: ClickHouse for massive volumes (100M-10G rows), Trino/Presto for complex multi-source joins, DuckDB for ad hoc analyses. Standard PostgreSQL suffices up to a few million rows. Performance tests from POC phase.

Pitfall 04

Neglecting row-level security (RLS). Power BI has well-integrated native RLS. Superset offers mechanisms but requires more manual configuration.

ATLAS response

RLS via SQL: filters in views (by authenticated user via Superset RBAC), or Superset RLS configured per dataset. For complex cases (multi-level dynamic RLS), may require custom development. Security parity tests vs Power BI on all user roles.

Related expertise

Migration & Data

See modules, services, and use cases.

Proprietary methodology

ATLAS methodology

10 steps, 9 principles, proven parity.

Cell composition

A specialized cell for this modernization path.

Several distinct profiles, mobilized over the full program duration. Reproducing this cell internally is rarely realistic — the legacy skills shortage and ATLAS expertise depth make outsourcing structurally faster and less risky.

Duration

Comparative audit: 3 weeks · Full migration: 6-12 months if Go decision

Volume

4 to 6 people in migration · 2-3 people in audit-only phase

Profiles

Data & BI Architect

Power BI tabular modeling (VertiPaq, DAX) AND SQL/dbt modeling on Superset side, target analytical stack design

Superset / Python developer

Superset configuration, dashboards, RBAC, RLS, Python customs if needed, self-hosted or Preset deployment

SQL & data engineering developers

Model reconstruction via SQL views or dbt, analytical database choice (PostgreSQL, ClickHouse, Trino, DuckDB), materialized tables

BI business referent

Per critical dashboard need-redefinition workshops, reconstructed dashboard validation, editor change management

Security referent (RLS / RBAC)

1 (half-time)

Power BI RLS → SQL RLS + Superset RBAC mapping, per-user-role security parity tests

Analytical DBA

1 (per target)

ClickHouse / Trino / PostgreSQL tuning, ingestion, indexing, dashboard performance monitoring

Access field experience

This path in real conditions.

Access capability — multi-stack BI

Proven capability on multi-BI: Power BI (Pentaho → Power BI public sector NA, France telecom Local CDR, multi-country Dynamics retail), Databricks data engineering. Apache Superset / open source capability applicable to organizations seeking a sovereign alternative to Power BI.

Power BI expert · Superset capability · applicable open source stack

Frequently asked questions

What decision-makers ask about this path.

Why migrate Power BI to Superset?+

Three legitimate reasons. Technological sovereignty (European public bodies, defense, healthcare). Premium license cost on large estates (10,000+ users). Strict on-premise deployment (regulated zones, ultra-sensitive data). Outside these cases, Power BI typically remains the right choice.

Is Apache Superset really at Power BI's level?+

On visualization features, Superset is at the level (charts, dashboards, filters, drill-down). On semantic model, Power BI has the advantage (DAX, reusable measures) — Superset compensates via dbt and SQL. On usage simplicity, Power BI remains more polished. On massive performance (100M+ rows), Superset + ClickHouse can surpass Power BI.

How much does a Power BI → Superset migration cost?+

Cost depends on the number of dashboards actually in use, on whether the semantic layer is centralized (a shared model migrates far better than a multitude of local ones), and on the number of business domains involved. Moving to Superset removes licenses but shifts cost toward platform operations: that item is priced separately. Free initial scoping call, 30 minutes to 2 hours.

Can we keep Power BI and use Superset as complement?+

Yes, this is even a common strategy. Power BI for standard business uses (Microsoft 365, Excel, Teams integration). Superset for sovereign, on-premise dashboards, or massive analyses on ClickHouse. The data model can be common (shared lakehouse).

Considering a Power BI → Superset migration?

Three concrete ways to start — from a 3-week comparative audit to a full migration program. Our approach systematically begins with a documented Go/No-Go audit before any migration commitment, because outside the sovereignty/cost/on-premise cases, Power BI remains the right choice in most situations.

Comparative audit (3 weeks) →AI Data & Automation expertise →Build-Operate-Transfer model →

Case studies