Executive Summary

Analytics engineers and data platform teams using dbt increasingly struggle to navigate large manifests and complex DAGs, costing time on impact analysis, debugging, and onboarding as projects grow from dozens to hundreds of models. That pain is amplified by the lack of a live, interactive UI that ties model code, tests, and runtime metadata together for fast exploration and decision-making. You could build an OSS-first interactive UI (with a hosted option) that ingests dbt manifests and live metadata from cloud warehouses and orchestrators to visualize lineage, show downstream impact, enable version diffs, and run ad hoc tests from the interface. Focus on lightweight connectors, a performant graph explorer with search/filter, and exportable lineage artifacts for governance and handoffs. The market looks compelling: an estimated $6.0B TAM (200,000 analytics teams × $30K ACV), a market score of 90/100, and strong tailwinds from accelerating dbt adoption and managed warehouse/orchestration make hosted metadata ingestion practical. That said, converting OSS users to paid customers and handling varied deployment environments are real challenges that must be addressed early. You can differentiate by leveraging an open-source core to capture the natural OSS funnel, delivering seamless live integrations with modern warehouses/orchestrators, and emphasizing a UX that measurably reduces debugging and onboarding time, while adding enterprise features (access controls, audit logs) to justify hosting fees.

Market Opportunity

Visualize and explore dbt projects and lineage with an interactive UI targets a $6.0B = 200,000 analytics teams × $30K ACV total addressable market with medium saturation and a year-over-year growth rate of 20% YoY — derived from modern data stack tooling adoption and dbt ecosystem growth reports.

Key trends driving demand: dbt adoption is increasing — as more teams adopt dbt they need richer tooling to navigate large manifests and DAGs.; Shift to managed data warehouses and orchestration — easier connectivity makes hosted metadata ingestion and live UIs practical.; Open-source-first buying patterns — analytics teams often adopt OSS tooling before converting to hosted versions, creating a natural funnel.; AI-assisted code and metadata understanding — vector search and LLMs make model discovery, impact analysis, and automated suggestions feasible..

Key competitors include dbt Labs (dbt docs / dbt Cloud), Lightdash, DataHub, Datafold.

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Visualize and explore dbt projects and lineage with an interactive UI

Executive Summary

Market Validation

Market Opportunity

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