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Fabric Lens connects the technical details behind Microsoft Fabric and Power BI assets so enterprise IT can manage change, reduce risk, and allow teams to focus on building.
Catalog and estate intelligence
Maintain an up-to-date record of workspaces, capacities, tenant settings, personal workspaces, apps, audiences, gateways, and connections. Scheduled scans keep it aligned as the tenant changes, and deleted or removed items are cleaned out of the catalog automatically, so it doesn't accumulate stale entries.
View tenant capabilitiesCatalog semantic models, dataflows, reports, notebooks, lakehouses, warehouses, and the technical definitions behind them.
Explore semantic modelsGenerate business-wiki and technical documentation from the current tenant state, including ownership, dependencies, metadata, and operational context. Each document is flagged stale once the object it describes changes, and carries a quality badge showing whether anyone has reviewed it.
Explore documentationFind assets, expressions, dependencies, and governance information across the catalog from a single place.
Browse expressions tenant-wideLineage and impact analysis
Follow dependencies through report visual bindings, DAX, semantic models, notebooks, pipelines, dataflows, queries, and source objects.
Explore data lineageExtend lineage beyond the Fabric tenant with remote plugins that follow dependencies into SQL Server and Databricks workloads.
Explore remote lineageAssess downstream reports, semantic objects, and source dependencies before changing a schema, model, or business definition.
Plan a source changeGovernance and trust
Review effective access across workspaces, apps, and individual assets, and search by person or by asset. Access granted through a security group is resolved down to the person, so group-based access shows up in both views.
Explore access trackingReview table relations, measure convergence, and the model objects that define shared business metrics. Measures are grouped from exact duplicates down to variants and siblings, so conflicting definitions of the same metric stand out first.
Explore measure convergenceIdentify broken DAX and broken report visuals before they affect users or erode confidence in the numbers.
Explore DAX diagnosticsApply rule packs for naming, ownership, and labeling alongside technical diagnostics. Every rule can be previewed against real objects, with a per-rule score breakdown, before it's applied tenant-wide, and Lens Score turns the results into a quality score for each model, measure, and column.
Explore rule packsRationalization and performance
Compare semantic models and reports using their definitions, shared objects, and physical lineage to identify consolidation candidates. Overlapping models are grouped into ranked clusters, with a likely hub model to start from.
Explore model similarityIdentify unused columns and the lineage behind them, so teams can see the downstream impact before cutting anything. A column only counts as unused once its full dependency chain, including visuals, filters, and relationship keys, has been checked.
Explore model cleanupAssociate capacity consumption and refresh schedules with the assets responsible for the work. Consumption rolls up to 7- and 30-day trends per item, ranked so the heaviest consumers surface first, and a refresh timeline compares each model's schedule with what actually ran.
Explore capacity metricsConnect report opens, exports, subscriptions, and Excel access to the content and users involved, retained well beyond Fabric's own ~28-day activity window. Once detailed history ages out, a summary of event counts and last-activity dates remain for each asset.
Explore usage analysisAutomated operations
Run scheduled metadata, dependency, access, and identity analysis so the catalog reflects the current tenant.
Explore metadata syncExtract and resolve technical definitions into the relationships used for lineage, impact analysis, documentation, and diagnostics.
Explore dependency resolutionRun analysis, documentation, remote-lineage, and diagnostic jobs on a repeatable schedule, not ad hoc.
Explore operational jobsAI enablement
Expose read-only tools to enterprise assistants and agents through the Model Context Protocol. Catalog tools search objects by name, return an object's context and dependency edges, and walk the graph to answer where data comes from or what a change would affect. Model tools rank candidate semantic models for a business question before any query is written.
Ground DAX written by people or agents in the selected model: question-matched example queries, preferred measures, common pitfalls, and relationship and grain guidance. A draft query is checked against the read-only policy and the model's semantic guidance, and only a validated query runs, within strict limits.
Supply the context an AI system works from, packaged per model: tables, measures, and columns, the generated wiki summary, DAX guidance, and visual-to-source provenance. Fabric Lens acts as the retrieval and grounding layer for Copilot, RAG, and agent initiatives, and the chat experience stays with the AI system you already use.
Connected platforms
Spotfire, Tableau, and SSRS are scanned into the same catalog and dependency graph as Fabric and Power BI, so a mixed estate is inventoried and compared in one place. Remote lineage plugins continue the dependency analysis past the tenant edge into SQL Server and Databricks.
Explore integrationsCataloged alongside Fabric and Power BI
Lineage extended into
Next step
Discuss the assets, governance questions, and lineage depth that matter to your organization.