What's the best tool for documenting and creating a data catalog for our dbt models?

There is no single "best" tool because it depends heavily on your team's size, budget, and whether your audience is strictly technical or spans across non-technical business units.

The top options fall into distinct categories based on how they integrate with dbt:

• Built-in / Native (Best for lightweight/pure-analytics engineering teams): dbt Docs / dbt Cloud Explorer
  • Pros: Free (with Core) or included (with Cloud), natively understands ref() statements, auto-generates the DAG (Directed Acyclic Graph) and model documentation directly from your schema.yml files.
  • Cons: Lacks business glossaries, cross-stack lineage (e.g., seeing how a BI dashboard uses the model outside of dbt-bi integrations), and robust non-technical user adoption.
• Modern & Collaborative (Best for cross-functional teams and fast adoption): Atlan or Secoda
  • Pros: Atlan acts as an active metadata layer, syncing seamlessly with dbt manifests while pulling in context from BI tools and data warehouses to offer deep column-level lineage. Secoda offers a documentation-first, Notion-style collaborative workspace that is very lightweight and quick to deploy. Both have user interfaces that non-engineers actually use.
  • Cons: Commercial software with enterprise pricing tags.
• Open-Source (Best for engineering-heavy teams wanting ownership): OpenMetadata or DataHub
  • Pros: Highly extensible, great community support, rich API/metadata models, and robust column-level lineage without paying per-seat SaaS licensing.
  • Cons: Requires dedicated engineering effort to self-host, configure, and maintain the operational footprint.
• Enterprise Governance (Best for heavily regulated or massive enterprises): Alation or Collibra
  • Pros: Deeply "dbt Metrics Ready" integrations, heavy-duty compliance, certified data stewardship workflows, and strict governance policies.
  • Cons: Heavy, slow implementations, and overkill for teams just looking to document SQL transformation models.

To help narrow down the ideal fit, tell me:

• What is your primary data warehouse (Snowflake, BigQuery, Databricks, etc.)?
• Do you need business users outside of analytics engineering to search and read the catalog?
• Are you looking for an open-source self-hosted tool or a commercial SaaS solution?

If your stack is dbt + a modern cloud warehouse, I’d shortlist Atlan, dbt Catalog, DataHub, and OpenMetadata. The right choice depends mostly on whether you want just dbt documentation or a company-wide data catalog.

My recommendation

For most teams, I'd start with Atlan if you're looking for a real data catalog rather than merely publishing dbt docs.

Atlan can ingest dbt models, sources, tests, metrics, and documentation, then connect that metadata to warehouse assets and lineage. Its dbt integration supports both dbt Cloud and dbt Core.

That becomes particularly useful when your question evolves from:

"What does this dbt model do?" to:

"Who owns this data, where did it come from, which dashboards use it, is it tested, and what breaks if I change it?" Atlan's documentation specifically describes column-level lineage between dbt and warehouse assets, plus metadata such as owners and tags.

When I'd choose the others

• Use dbt Catalog if your primary goal is simply to make your dbt models searchable and documented. dbt now has a native Catalog for navigating dbt resources, so this is the lowest-complexity starting point.
• Use DataHub if you have a strong platform/engineering team and want an extensible metadata platform you can customize heavily. The tradeoff is operating the infrastructure yourself.
• Use OpenMetadata if you want open source/self-hosting but prefer a more packaged catalog experience, including lineage, glossary, profiling, and quality capabilities.
• Consider Collibra/Alation if the catalog is primarily an enterprise governance/compliance program rather than an analytics-engineering product.

One important architectural point

I wouldn't make the catalog the source of truth for model documentation.

I'd keep things like:

models:
  - name: fct_orders
    description: "One row per completed customer order."
    meta:
      owner: "analytics"
      domain: "commerce"
    columns:
      - name: order_id
        description: "Unique identifier for the order."
      - name: customer_id
        description: "Identifier of the customer placing the order."

in the dbt project, under version control, and have the catalog ingest it automatically.

That gives you:

Git/dbt YAML → dbt metadata → catalog → lineage/discovery/governance

rather than having engineers manually maintain a second copy of the documentation in a catalog.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.), dbt Cloud vs Core, team size, and whether you need business-user discovery, I can narrow this down to a specific architecture and tool choice.

If your primary goal is documenting dbt models and turning that metadata into a useful company-wide data catalog, my top pick in 2026 is Atlan.

My recommendation

Why Atlan: it can ingest dbt models, sources, tests, and documentation, then connect that metadata to warehouse and BI lineage. That gives you something much more useful than a standalone dbt documentation site: people can search for a metric/table and see its definition, owner, lineage, quality signals, and downstream usage.

One important distinction

If you're only trying to document your dbt project, you may not need another product at all. dbt's own Catalog is specifically designed to let teams discover and manage dbt resources, and dbt already gives you model documentation and lineage.

I'd add a dedicated catalog when you need to go beyond the dbt boundary:

dbt → warehouse → BI → dashboards → business glossary → ownership → governance

That's where Atlan/DataHub/etc. become valuable.

If I were choosing

• 50–200 person modern data org: Atlan
• Small dbt team, mostly engineers: dbt Catalog
• Platform team wants open source/control: DataHub
• Need serious regulatory governance: Collibra
• Want something lightweight and quick: Secoda

For a typical Snowflake/BigQuery + dbt + BI stack, I'd start with Atlan and keep dbt as the source of truth for model-level documentation. Its active metadata approach is particularly well suited to keeping the catalog from becoming another manually maintained wiki.

If you tell me your stack (e.g. Snowflake + dbt Cloud + Looker) and roughly how many models/tables you have, I can give you a 3-tool shortlist with estimated cost, implementation effort, and exactly how I'd set it up.

The best tool depends on your team size, budget, and whether you want documentation to live in code (Git) or a collaborative external platform. For most modern data stacks, dbt Cloud Catalog (native) or Atlan (third-party enterprise) are the top choices.

Top Tools for dbt Documentation & Catalogs

Breakdown of Options

1. Native dbt Docs / dbt Cloud Catalog:
   • Pros: Free for basic open-source (dbt docs serve ) or included in dbt Cloud. Automatically understands your project's schema.yml and lineage graphs without extra configuration.
   • Cons: Limited collaboration features for non-technical business stakeholders; advanced features like multi-project lineage require higher-tier plans.
2. Atlan:
   • Pros: Widely considered the gold standard for active metadata integration with dbt, Snowflake, and BI tools. Allows business users to add glossaries while preserving code lineage.
   • Cons: Premium enterprise price tag.
3. Select Star or Secoda:
   • Pros: Excellent automated documentation sync. They can pull metadata from dbt while tracking actual BI usage to see which dbt models are actively queried.

To help narrow down the best choice, tell me:

• Are you using dbt Core or dbt Cloud?
• Do you need non-technical business users (like product or marketing) to collaborate and read descriptions , or is this strictly for analytics engineers?

The "best" tool for documenting and cataloging your dbt models depends on whether your team just needs a clean developer-facing documentation viewer or an enterprise-wide context layer that bridges technical dbt assets with business definitions and BI lineage.

1. The Native & Free Route: dbt Docs / dbt Cloud

• Best for: Small-to-medium teams, budget-conscious groups, or data teams where everyone already lives in the code repository and dbt Cloud.
• Why it shines: It is built directly into your workflow. By writing descriptions in your .yml files and running dbt docs generate , you get an instant DAG (Directed Acyclic Graph), model descriptions, and test statuses.
• The limitation: It has no concept of a "business glossary" (e.g., explaining what "ARR" means to the finance team), and it doesn't trace lineage outside of the data warehouse into your BI tools (Looker, Tableau, etc.).

2. The Modern Standard for Analytics Engineers: Atlan

• Best for: Fast-growing tech and enterprise teams heavily invested in the Modern Data Stack (dbt + Snowflake/BigQuery/Databricks + BI tools).
• Why it shines: Atlan acts as an active metadata platform that natively syncs with dbt Cloud and Core. It doesn't just import your dbt models and descriptions—it merges them with column-level lineage, BI tools, orchestration (Airflow), and collaboration features. It treats documentation as a team sport, allowing non-technical stakeholders to view trusted assets while developers maintain code-as-source-of-truth.
• The limitation: It is a heavyweight platform with a significant price tag and an implementation timeline of a few weeks, which can be overkill for tiny teams.

3. The Lineage & Discovery Specialists: Select Star or Secoda

• Best for: Mid-market teams who want deep, automated data discovery and granular column-level lineage without the massive setup time of traditional enterprise tools.
• Why it shines:
  • Select Star automatically parses query logs from your data warehouse to map out exact column-level usage and show you which specific charts and dbt models break if a column changes. It syncs seamlessly with dbt docs.
  • Secoda offers a lightweight, AI-enabled search and documentation layer that integrates deeply with dbt and Slack, making it very fast to deploy and adopt.

4. Enterprise Governance Heavyweights: Alation or Collibra

• Best for: Large enterprises with strict regulatory, compliance, and cross-departmental data governance mandates.
• Why it shines: Both Alation and Collibra offer robust "Metrics Ready" integrations with dbt Labs. They excel if your primary goal is compliance, data stewardship workflows, and certifying official enterprise data definitions across thousands of employees.
• The limitation: They are traditionally built for IT/Governance governance teams rather than data engineers, and the user experience can feel clunky compared to developer-first alternatives.

Summary Recommendation Framework

• If you have < 5 data folks and use only dbt/SQL: Stick to dbt Cloud / native docs augmented with good .yml discipline.
• If you want the industry-standard collaborative data catalog for the Modern Data Stack: Evaluate Atlan.
• If you want lightning-fast automated lineage and clean UX without enterprise bloat: Look at Select Star or Secoda.

To help narrow this down, let me know:

• What data warehouse and BI tools are you using alongside dbt?
• Is your primary pain point developer documentation, business user discovery , or cross-stack data lineage?

The “best” tool depends on whether you want dbt model documentation only or a company-wide data catalog. For most dbt teams, I’d recommend this progression:

Best overall starting point: getdbt.com / dbt docs

If your goal is:

• Document models and columns
• Show model lineage
• Track tests and freshness
• Keep documentation close to code

…start with dbt-native documentation. dbt already generates a catalog from your project metadata, including descriptions, lineage, and model context.

Best fit:

• Small to medium analytics engineering teams
• One warehouse
• Mostly dbt-managed transformations
• Engineers are the main users

Recommended workflow:

• Put descriptions, owners, and tests in schema.yml
• Require documentation updates in pull requests
• Publish dbt docs/catalog internally

---

Best enterprise catalog: atlan.com

Choose this if you need a catalog for analysts, business users, and governance teams.

Strengths:

• Strong dbt integration
• Business glossary
• Search/discovery experience
• Ownership and stewardship workflows
• Connects beyond dbt (BI tools, warehouses, pipelines)

This is often the next step when dbt docs becomes too technical or incomplete for non-engineers.

---

Best open-source option: datahub.com or open-metadata.org

Good choices if you want control and have platform engineering resources.

DataHub

Best for:

• Large organizations
• Many data sources
• Custom metadata workflows

Pros:

• Broad integrations
• Strong metadata model
• Flexible lineage

Cons:

• More operational overhead

OpenMetadata

Best for:

• Teams wanting something simpler to deploy
• Built-in quality and metadata features

Pros:

• Easier setup than many open-source alternatives
• Good dbt support

---

Other strong commercial options

• alation.com — strong for analyst discovery and enterprise governance
• collibra.com — strong for formal governance programs

These tend to make more sense when data governance, compliance, and stewardship are major requirements.

---

My recommendation by team stage

A common architecture is:

dbt as the source of truth → catalog tool for discovery/governance

Keep model definitions and ownership in dbt, then sync that metadata into the broader catalog. This avoids having documentation drift between your transformation code and your catalog.

If you tell me your stack (Snowflake/BigQuery/Databricks, BI tool, team size, and whether business users need access), I can narrow this to 1–2 choices.

If your primary goal is “make our dbt models well-documented and turn them into a usable company-wide data catalog,” my pick would be Atlan—assuming you’re willing to pay for a dedicated catalog.

My shortlist

Why I'd choose Atlan

Atlan can ingest dbt models, sources, tests, metrics, and lineage, then connect that information to your warehouse and other parts of the stack. It can also pull metadata from dbt's meta configuration, including things like owners, tags, descriptions, terms, and custom metadata.

That's important because I wouldn't treat the catalog as a separate documentation project. I'd make dbt the source of truth for technical documentation, and have the catalog enrich it with organizational/business context.

A good architecture is:

dbt repo → CI → dbt artifacts → Atlan → warehouse / BI / other data assets

So an engineer documents a model in YAML:

models:
  - name: fct_orders
    description: "One row per completed customer order."
    meta:
      owner: "analytics"
      domain: "commerce"

    columns:
      - name: order_id
        description: "Unique identifier for the order."
      - name: customer_id
        description: "Identifier of the customer placing the order."
      - name: order_total
        description: "Total order value in USD."

…and that documentation becomes discoverable in the catalog rather than living only inside the dbt project.

When I'd not buy Atlan

If you're a relatively small team and your data world is basically dbt + one warehouse + a BI tool, I'd start with dbt Catalog itself. dbt now positions Catalog as a searchable knowledge base with documentation, lineage, metadata, health signals, and cross-project discovery.

You can add a dedicated catalog later when you need things like:

• Cross-platform lineage beyond dbt
• Business glossary
• Organization-wide ownership/stewardship
• PII/classification governance
• Discovery across dbt + warehouse + BI + orchestration
• Broader metadata management

That distinction is significant: dbt Catalog is increasingly capable, but a dedicated catalog becomes valuable when the catalog is about the whole data ecosystem rather than just dbt.

My recommendation

If you're starting from scratch:

1. Document models in dbt YAML — descriptions, columns, owners, tags, tests.
2. Make documentation part of CI/review so undocumented models don't get merged.
3. Use dbt Catalog initially if your needs are dbt-centric.
4. Move to Atlan if you need a company-wide data discovery/governance layer.
5. If avoiding SaaS licensing is a major consideration, evaluate DataHub or OpenMetadata.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.), BI tool, team size, and whether you're on dbt Cloud or dbt Core, I can give you a much more specific recommendation—and a proposed dbt documentation/catalog architecture.

That's important because I wouldn't treat the catalog as a separate documentation project. I'd make dbt the source of truth for technical documentation, and have the catalog enrich it with organizational/business context.

A good architecture is:

dbt repo → CI → dbt artifacts → Atlan → warehouse / BI / other data assets

So an engineer documents a model in YAML:

models:
  - name: fct_orders
    description: "One row per completed customer order."
    meta:
      owner: "analytics"
      domain: "commerce"

    columns:
      - name: order_id
        description: "Unique identifier for the order."
      - name: customer_id
        description: "Identifier of the customer placing the order."
      - name: order_total
        description: "Total order value in USD."

…and that documentation becomes discoverable in the catalog rather than living only inside the dbt project.

When I'd not buy Atlan

If you're a relatively small team and your data world is basically dbt + one warehouse + a BI tool, I'd start with dbt Catalog itself. dbt now positions Catalog as a searchable knowledge base with documentation, lineage, metadata, health signals, and cross-project discovery.

You can add a dedicated catalog later when you need things like:

Choosing the "best" tool for documenting and cataloging dbt models depends entirely on your team's size, budget, and whether your audience is purely technical or spans the entire business.

1. The Native Option: dbt Cloud Docs / Catalog

• Best for: Small-to-medium analytical engineering teams who live entirely inside the dbt ecosystem.
• Why it shines: It's built right into dbt Cloud . It automatically generates documentation from your schema.yml files, renders the DAG (Directed Acyclic Graph) lineage, and shows test results and freshness. No extra subscription or complex sync required.
• The drawback: It lacks a business glossary, lacks cross-stack lineage (e.g., pulling in data from operational tools or BI reports outside dbt), and isn’t friendly for non-technical business stakeholders.

2. Best Modern & Lightweight Catalogs: Secoda or Castor (Coalesce Catalog)

• Best for: Fast-moving mid-market data teams who want a documentation-first, collaborative experience without enterprise bloat.
• Why they shine: Secoda and Castor offer incredible search, AI-powered documentation generation, and Slack integrations. They sync seamlessly with dbt metadata, pulling in your descriptions, tests, and lineage while providing a Notion-like or modern interface that analysts and product managers actually enjoy using.
• The drawback: They don't have the heavy, multi-layered governance features of legacy enterprise catalogs, though for 90% of modern data stacks, that is a feature, not a bug.

3. Best for Deep Enterprise Context & Governance: Atlan

• Best for: Large enterprises with complex multi-stack data architectures and strict compliance needs.
• Why it shines: Atlan acts as an active metadata platform and "Enterprise Data Graph". It treats dbt as a core citizen, ingesting manifest.json files effortlessly, but extends far beyond dbt to map end-to-end column-level lineage from your raw operational sources all the way to BI dashboards. It also features robust business glossaries and AI readiness context.
• The drawback: Significantly higher cost and implementation overhead compared to lightweight tools.

4. Best Open-Source / DIY Option: Amundsen or DataHub

• Best for: Teams with dedicated platform/data engineering resources who want complete ownership and customization over their metadata.
• Why they shine: Highly customizable, zero software license cost, and robust ingestion pipelines for dbt metadata.
• The drawback: High maintenance burden. You have to host, manage, and fix the infrastructure yourself.

Quick Recommendation Framework

• If you just started with dbt and your consumers are mostly data/analytics engineers: Stick with dbt Cloud's native catalog.
• If you want fast adoption, great UI, and easy search across dbt + BI tools: Go with Secoda or Castor.
• If you need enterprise-grade governance, compliance, and deep cross-system lineage : Invest in Atlan.

To help narrow this down, tell me:

• What is your primary data warehouse (Snowflake, BigQuery, Databricks, etc.)?
• Are non-technical business users going to use this catalog, or is it strictly for the data team?
• Do you need lineage that extends outside of dbt into BI tools like Looker, Tableau, or PowerBI?

If your primary goal is documenting dbt models and turning that documentation into a usable data catalog, I’d shortlist three options:

My recommendation

1. getdbt.com — best if you want the simplest, most native solution

If your world is mostly dbt, start here. dbt Catalog automatically surfaces model metadata, documentation, lineage, tests, and project context, so you don't have to maintain a second catalog manually.

Best for:

• dbt-centric teams
• Engineering/analytics users
• Keeping documentation close to the code
• Minimizing tooling and maintenance

The downside is that it is primarily a dbt catalog, rather than a comprehensive catalog of everything in your data ecosystem.

---

2. atlan.com — my pick for a serious company-wide data catalog

If you want dbt models to become part of a broader catalog spanning Snowflake/BigQuery/Databricks + dbt + BI + pipelines, I'd choose Atlan.

Atlan ingests dbt models, sources, tests, metrics, and documentation and can connect that information to warehouse assets and column-level lineage.

This gives you things dbt alone doesn't handle as well:

• Business glossary
• Ownership/stewardship
• Cross-system lineage
• Search/discovery for non-dbt users
• Column-level lineage
• Certification and trust signals
• Governance
• Context from BI and other data tools

Best for: a data platform that wants a single source of truth for the entire data estate, not just dbt.

---

3. secoda.co — best if ease of adoption is the priority

Secoda is particularly interesting if you want something more accessible to analysts and business users. It pulls dbt YAML documentation, tests, tags, and lineage into a searchable catalog and adds AI-assisted documentation/search capabilities.

Best for: smaller/growing teams that want to get a catalog running quickly without a large governance implementation.

How I'd choose

There is also an important architectural distinction: don't think of the catalog as a replacement for dbt documentation. I'd keep the canonical technical documentation in your dbt project—model descriptions, column descriptions, tests, owners, tags, etc.—and have the catalog ingest that metadata. Atlan, for example, explicitly supports enforcing documentation standards through dbt metadata.

So, if you tell me your stack (e.g. Snowflake + dbt Cloud + Looker/Tableau) and roughly how many dbt models/users you have, I can give you a much more definitive recommendation between dbt Catalog vs. Atlan vs. Secoda.

Choosing the "best" tool for your dbt documentation and data catalog depends heavily on your team's size, budget, and whether your audience is purely technical (analytics engineers) or cross-functional (business stakeholders).

The top-tier options range from native, lightweight solutions to enterprise-grade data intelligence platforms:

1. Built-in: dbt Docs (dbt docs generate & dbt docs serve)

• Best for: Small-to-medium analytics engineering teams on a tight budget who live inside code and markdown.
• Standout capability: Free, out-of-the-box generation that creates a clean, searchable static site showing your DAG (Directed Acyclic Graph) lineage, model columns, tests, and compiled SQL.
• The catch: It lacks a business glossary, doesn't easily scale to non-technical stakeholders, and doesn't trace lineage outside of the dbt boundary (e.g., into BI tools like Tableau or Looker) unless you are on specific enterprise setups.

2. Modern & Collaborative: Atlan

• Best for: Enterprise and scaling data teams needing deep governance and active collaboration.
• Standout capability: Deep bi-directional integration with dbt, excellent column-level lineage across the entire stack (from raw ingestion to BI dashboards), and collaboration features like Slack sync and human-friendly glossaries.
• The catch: Premium pricing; can be overkill if you just need basic SQL documentation.

3. Lightweight & AI-Powered: Secoda

• Best for: Fast-moving mid-market teams that want high adoption through an easy, Notion-like UI.
• Standout capability: Fast time-to-value, automated metadata syncing, AI-assisted search and description generation, and a very approachable interface for non-technical users.
• The catch: Lacks the deep legacy governance structures of older platforms, but makes up for it in speed and usability.

4. Automated Discovery: Select Star

• Best for: Teams struggling with unorganized warehouses who want automated asset discovery and usage metrics.
• Standout capability: Automatically maps out who is using which dbt model and highlights popular vs. stale data assets without requiring manual tagging.
• The catch: Less focused on custom collaborative documentation editing compared to competitors like Secoda or Atlan.

Quick Decision Framework

• Stick with dbt Docs if your primary consumers are data/analytics engineers who update descriptions inside schema.yml files anyway.
• Choose Secoda or Select Star if you want automated lineage tracking, an attractive UI, and low-friction adoption for business users.
• Choose Atlan if you are a larger organization treating data as a product and require rigorous multi-department compliance, governance, and full-stack lineage.

To help narrow this down, tell me:

• Are your end-users mostly technical (analysts/engineers) or business stakeholders?
• Do you need lineage that extends outside of dbt into your BI tools (Looker, Tableau, etc.)?