Apache Superset or Power BI: our data architects' analysis

It depends on your organisation and on the maturity of your data teams. For a team with SQL-literate analysts and operating capacity, the constraints stay manageable. For an organisation whose business users build their own reports, the absence of a central semantic model weighs more heavily: each dashboard carries its own SQL, where Power BI shares its measures. Operating a platform made of several services adds to this, and the project maintains only its two latest versions. These constraints ease considerably once each metric is defined a single time in versioned SQL, which makes every output dependable. The ATLAS methodology builds that layer in from the scoping phase.

It depends on how strictly your data must be partitioned. For a small team with no per-user confidentiality requirement, Metabase deploys quickly and suits less technical profiles. As soon as data must be partitioned by user profile, the free edition of Metabase reaches its limit: row and column security is reserved for its paid plans, self-hosting included. Apache Superset includes row-level security and offers finer access control, at the cost of more demanding operations. The choice therefore rests on your security model ahead of any interface preference. Our data architects establish it during the comparative audit.

It depends on each user's role. Reading a dashboard requires no technical skill: filters and exploration remain available to business teams. Building new dashboards calls for SQL, since Apache Superset relies on direct queries where Microsoft Power BI offers assisted modelling. This criterion determines who, within your organisation, keeps their autonomy. Once metrics are defined a single time upstream, business analysts reuse prepared datasets and their reliance on SQL falls markedly. Our data architects assess this point before any migration decision.

Automatic conversion does not exist: Microsoft Power BI visuals do not transpose as they are into Apache Superset. Each dashboard genuinely in use is rebuilt, after a short workshop with the business teams to confirm how it is used. This step is an opportunity to rationalise the reporting estate, as some legacy reports may no longer be consulted. The ATLAS methodology frames this rebuild with signed parity tests, which evidence that every metric returns the same result as before the migration.

It depends on your in-house operating capacity. An organisation with an experienced platform team can run it itself. For organisations without one, the workload covers regular upgrades, monitoring, backups and tuning of the analytical database. The profile required is platform engineering rather than BI administration. Access International provides this operation over time with its nearshore teams, as a partner to your IT department. The operating model is defined during scoping, so that the decision to migrate accounts for the full running of the platform.

It depends on your constraints, and migration is not always desirable. It is warranted when data sovereignty requires hosting on your own infrastructure, or when a large read-only audience makes per-user billing decisive. Outside these situations, Microsoft Power BI generally remains the sounder choice. Beyond the change of platform, migration is an opportunity to redefine metrics on a shared, versioned basis, and to open the reporting estate to artificial intelligence tools. The comparative audit establishes, before any commitment, whether migration is warranted in your context.

It depends on the criterion considered. Apache Superset offers a broad catalogue of charts and interactive dashboards. The difference lies mainly in modelling: Microsoft Power BI has a central semantic model and reusable measures, which Apache Superset offsets with a layer of metrics defined in SQL. On large volumes, performance depends on the analytical database behind Apache Superset. No independent public benchmark settles the comparison on that ground, and only a test on your own data gives a dependable answer. The comparative audit includes that test.

It depends on the scope genuinely involved. Cost is established from the number of dashboards actually in use and from how centralised the semantic layer is. A shared data model migrates far more efficiently than a multitude of local ones. Removing licences shifts the load towards operating the platform, which belongs in the analysis from the outset. Access International communicates no figure before scoping: the comparative audit produces an estimate grounded in your real reporting estate.

It depends on how your uses are distributed, and this coexistence can prove sound. Microsoft Power BI then keeps the everyday uses embedded in Microsoft 365, while Apache Superset takes on the dashboards bound by a sovereignty requirement. This hybrid architecture rests on one condition: both platforms must rely on the same metric definitions, failing which one metric would return two different results. Our data architects design that shared layer, which guarantees consistent figures whichever platform is consulted.