In summary, Tableau user filters correspond to Power BI’s model-based Row Level Security, which utilizes DAX roles and identity context. Pair Unli Casino it with SSO, audit logging, and column masking; see data governance for AI-powered BI for the wider checklist. Several tools (Sigma, Omni, Lightdash, Metabase, Basedash on Postgres) are designed around that single-database, attribute-filtered pattern. Most SaaS analytics use one database with a tenant_id column and row-level filtering.

In-memory engines (Power BI import mode) evaluate the filter quickly; DirectQuery and live-query tools push it to the database, where an indexed equality filter is cheap and a per-row function call is not. For enforcement that does not depend on the BI tool, database-layer RLS in PostgreSQL (used by Basedash through the basedash.groups session variable) or Snowflake row access policies are stronger because every client is filtered. If you embed dashboards in your product, the tool must accept identity from your application (JWT, signed embed URL, trusted authentication) and set attributes per session. Snowflake has its own row access policies, and the simplest secure design is to enforce them in Snowflake and have the BI tool connect with a role or session context Snowflake can evaluate. Avoid Looker, ThoughtSpot Enterprise, and Tableau Enterprise at this stage unless a specific integration requires them; their RLS is excellent but the pricing and modeling overhead are sized for larger teams. Sandboxes restrict rows and can hide columns, and they apply to embedded dashboards and to Metabot AI questions.

The table compares nine platforms on the attributes that decide an RLS evaluation. Row-level security filters query results based on the authenticated user before data reaches a chart, dashboard, export, or AI answer. Pricing was re-verified against each vendor’s public pricing page in September 2026.

Equivalent Patterns For Row Level Security In Power Bi

ThoughtSpot and Looker have detailed system activity logs; Power BI needs Azure Monitor for query-level detail; Basedash and Metabase (Pro) ship audit logs; database-layer designs also leave a trail in the database’s own query log. The strongest implementations read attributes from your identity provider (Okta, Entra ID, Google Workspace) via SAML or OIDC and support SCIM so group membership stays in sync. Does RLS cover dashboards, exports, scheduled deliveries, API calls, embedded sessions, and AI-generated queries? For a broader tool comparison on that warehouse, see best BI tools for Snowflake. Sigma, Omni, ThoughtSpot, Looker, Tableau, Power BI (DirectQuery), Lightdash, and Metabase all query Snowflake live and can layer their own attribute-based filters on top. The IS NULL branch keeps your application’s direct connections working unchanged; only connections that set the variable (Basedash) are filtered.