Dataplex + Atlan: Unified Data Governance Across GCP & Beyond

What Dataplex Universal Catalog and Atlan Deliver Together: At a Glance

At a glance: Dataplex Universal Catalog vs Atlan vs Dataplex + Atlan

Capability	Dataplex Universal Catalog	Atlan	Dataplex + Atlan
Discovery	GCP assets only; technical interface.	Cross-platform, persona-based, Google-like search.	Unified discovery across GCP and every connected system.
Lineage	Column-level for BigQuery; table-level for external sources; 30-day retention.	Column-level across all connected systems; configurable retention.	End-to-end, automated column-level lineage from source to BI, with root cause and impact analysis.
Governance	IAM and policy tags within GCP perimeter.	Bidirectional policy propagation across all platforms.	Policies defined once in Atlan, enforced natively in GCP via Dataplex and across every other system.
Data quality	Built-in scanning for BigQuery via Data Metric Functions.	Cross-platform quality signals aggregated via Data Quality Studio.	Dataplex quality scans surface in Atlan alongside signals from dbt, observability tools, and external monitors.
Semantic layer	Semantic definitions scoped to BigQuery via Dataplex entries and aspects; no cross-platform metric standardization.	Enterprise-wide business glossary with machine-readable metric definitions, calculation rules, and terminology that works across all connected systems.	Dataplex semantic metadata ingested into Atlan and unified with definitions from dbt, Looker, and other semantic layers into a single authoritative source.
Collaboration	Not natively supported.	Embedded annotations, discussions, and review workflows.	Collaboration on all assets including GCP assets, driven through Atlan.
Persona-based experiences	Not supported.	Configurable per role and domain.	Business users, engineers, and compliance teams each get purpose-built views of the same metadata.
Data products	In preview; GCP-scoped.	Cross-platform data products with SLAs and access workflows.	Data products spanning GCP and non-GCP sources, governed through Atlan.
AI governance	Vertex AI asset cataloging; Gemini-powered insights within GCP.	MCP server delivering governed context to any AI tool at inference time.	AI agents consuming governed context from both Dataplex and the full enterprise stack via Atlan’s MCP server.
Adoption metrics	Requires custom configuration via Cloud Logging.	Native adoption dashboards and usage analytics.	Full adoption visibility across GCP and non-GCP assets in one place.
Architecture	Serverless, GCP-native, pay-as-you-go.	Sovereign, open, Iceberg-based metadata lakehouse; deployable on GCP, AWS, or Azure.	Dataplex as GCP governance backbone; Atlan as enterprise metadata control plane above it.
Context layer	Partial: technical metadata and governance within GCP perimeter; no cross-system context aggregation or AI-agent-ready delivery.	Full sovereign context layer: structural, operational, behavioral, and temporal context unified across the enterprise and delivered to AI agents via MCP server.	Complete enterprise context layer with Dataplex providing GCP-native context production and Atlan aggregating, enriching, and activating it across the full data and AI stack.
Best for	GCP-first organizations with BigQuery-centric stacks.	Multi-cloud organizations needing cross-platform governance.	Any organization running GCP alongside other platforms that needs unified governance without replacing native tooling.

What Does Google Cloud Dataplex Provide?

Google Cloud Dataplex Universal Catalog unifies metadata management for data and AI assets within the Google Cloud Platform ecosystem. The service automatically ingests technical metadata from BigQuery, Cloud Storage, Spanner, Vertex AI, Pub/Sub, Dataform, and Dataproc Metastore without requiring manual configuration.

The platform operates on three core metadata constructs:

Entries represent individual data assets like BigQuery tables or Cloud Storage buckets. Each entry carries system-generated technical metadata including schema definitions, data types, and resource identifiers.
Aspects attach structured business or governance metadata to entries. Organizations define custom aspect types to capture classifications, ownership information, data quality standards, or compliance tags.
Entry groups organize related entries by project, location, or business domain. These groupings support federated governance models where different teams manage their own data domains while maintaining centralized visibility.

Data Lineage in Dataplex

Dataplex automatically captures job-level and column-level lineage for BigQuery operations including CTAS, INSERT, MERGE, and VIEW definitions. Lineage from external tools like dbt and Airflow can be ingested via the OpenLineage-based Data Lineage API, though this is limited to table-level events. Lineage metadata is retained for 30 days and can take up to 24 hours to appear after job completion.

Data Quality in Dataplex

Dataplex includes built-in data quality scanning for BigQuery assets through Data Quality rules and Data Metric Functions. Quality scan results surface directly as catalog metadata, making freshness, completeness, and rule pass rates visible alongside the asset’s technical metadata.

Governance in Dataplex

Governance in Dataplex is enforced through Google Cloud IAM and policy tags. Policy tags applied to BigQuery columns control access at the field level. Tags and classifications are scoped to GCP assets and do not automatically propagate to systems outside the Google Cloud perimeter.

Dataplex Limitations

Three practical limitations constrain Dataplex for enterprise use:

Coverage stops at the GCP boundary: Dataplex doesn’t see dbt transformations, Tableau dashboards, Salesforce data, or on-premise databases unless teams manually extend lineage through the API using OpenLineage. This creates blind spots in the data flow map.
Business context requires manual enrichment: While Dataplex excels at technical metadata, teams must define glossary terms, document column meanings, and attach business ownership through separate processes. The catalog lacks collaborative features like inline discussions or automated propagation of business metadata along lineage paths.
The interface serves technical users primarily: Data analysts and business stakeholders often struggle with Dataplex’s console, which assumes familiarity with GCP concepts and doesn’t provide the search-first, Google-like discovery experience that non-technical users expect.

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How Does Atlan Extend Dataplex Capabilities?

Atlan sits above Dataplex as a sovereign enterprise context layer, ingesting Dataplex metadata alongside metadata from every other system in the stack, enriching it with business context, and activating it for both human teams and AI agents across the full data estate.

Think of Atlan as a federated, interoperable, multi-platform control plane, where Dataplex continues to govern GCP assets natively. Atlan extends that across the heterogenous stack of cloud platforms, BI tools, ETL tools, SaaS solutions, and AI agents.

Automated, Column-Level Lineage Across Platforms

Atlan stitches column-level lineage across every connected system into a single continuous enterprise data graph, connecting upstream sources through GCP transformations to downstream BI tools. Where Dataplex provides column-level lineage for BigQuery and table-level lineage for external sources, Atlan closes that gap across dbt, Airflow, Fivetran, Tableau, Snowflake, and Databricks without requiring custom connector development.

That same lineage graph powers:

Root cause analysis when data quality issues surface, tracing problems back through the pipeline to their origin.
Impact analysis before upstream changes are made, identifying every downstream asset and AI workflow that will be affected.

A Collaborative Workspace Driving Adoption Across Personas

Atlan embeds collaboration directly into the asset record: annotations, discussion threads, review workflows, and ownership assignments tied to specific tables and columns.

Persona-based experiences surface different views of the same metadata depending on who is asking, so data engineers, business analysts, compliance teams, and domain owners all work from the same governed layer without needing to navigate a technical interface designed for one audience.

Atlan’s platform also includes data products that package related datasets with documentation, quality SLAs, and access request workflows. This abstraction helps business teams discover and request data without understanding underlying technical complexity.

Automated Governance at Scale

Atlan’s automation engine continuously enriches metadata with AI-generated descriptions, classifications, ownership assignments, and policy tags. Governance decisions made in Atlan propagate bidirectionally to Dataplex and every other connected system, eliminating the manual synchronization that produces drift between the central catalog and individual platforms.

AI-Native Architecture

Atlan exposes metadata through a Model Context Protocol (MCP) server that AI agents consume directly. This enables intelligent operations like natural language search (“find all customer revenue metrics updated in the last week”), automated documentation generation, and proactive data quality monitoring that flags issues before users encounter them.

As a result, AI agents operating across GCP, Snowflake, and Databricks simultaneously consume the same governed context layer, grounded in authoritative definitions rather than raw schemas.

Metadata Lakehouse as the Open Context Store

The Metadata Lakehouse underneath Atlan stores all metadata in an open, queryable format.

This Apache Iceberg-based metadata lakehouse maintains lineage, governance policies, quality signals, glossary definitions, and usage patterns. You can query it via standard SQL by any Iceberg-compatible engine including Snowflake, Trino, Spark, and Athena.

Since it’s open and not locked inside a proprietary data model, any tool in your enterprise stack can query directly, keeping your enterprise in control of your metadata regardless of which platforms sit on top.

Data Quality Across the Full Stack

Atlan aggregates data quality signals from Dataplex’s built-in quality scans alongside quality metrics from dbt tests, observability tools, and external monitoring platforms with the Data Quality Studio.

Quality scores, freshness indicators, and rule pass rates surface alongside asset metadata across every connected system, not just BigQuery. This gives data teams and AI agents a consistent, cross-platform view of data fitness rather than fragmented quality signals that vary by platform and require manual reconciliation.

Deep, Two-Way Integration With GCP

Atlan maintains bidirectional synchronization with Dataplex, meaning metadata, tags, classifications, and policies flow in both directions. Dataplex governs GCP assets natively. Atlan ingests that metadata, enriches it, and syncs changes back, keeping both systems consistent without manual intervention. This makes the two platforms complementary by design rather than competing for the same governance role.

How Does the Dataplex-Atlan Integration Work?

The integration operates through a combination of direct API connections and automated enrichment packages that move metadata between the two platforms in near real-time.

Step 1: Initial Connector Setup

Teams configure authentication by creating a custom IAM role in Google Cloud with specific Dataplex permissions including dataplex.entries.list, dataplex.entries.get, dataplex.aspectTypes.list, and dataplex.aspectTypes.get. A service account assigned this role generates a JSON key that Atlan uses for read-only metadata access.

In Atlan’s interface, administrators navigate to Data Governance connectors and select Google Cloud Dataplex. They upload the service account key, specify GCP regions where aspects and entries exist, and optionally filter by BigQuery connection to control which metadata gets ingested.

The crawler runs on schedule or on-demand, extracting all Dataplex entries and their associated aspects. Results appear in Atlan’s asset search within minutes, tagged with Dataplex-specific metadata fields.

Step 2: Aspect Metadata Surfacing

Dataplex aspects become first-class metadata in Atlan’s interface. Users can filter global asset searches by aspect name, aspect key, or aspect value. On individual asset pages, a dedicated Dataplex metadata section displays all aspects associated with that entry, with additional filtering capabilities for detailed inspection.

The aspect structure defined in Dataplex translates directly to searchable, actionable metadata in Atlan.

Step 3: BigQuery Enrichment Packages

For deeper integration with BigQuery workflows, teams deploy Solutions packages that attach Dataplex metadata directly to BigQuery assets in Atlan. The Dataplex Enricher executes custom Dataplex queries to extract specific field values and creates or updates custom metadata structures in Atlan.

Configuration requires providing the GCP organization ID, service account JSON, a Dataplex query specifying which tags to extract, the target BigQuery connection in Atlan, and a list of Dataplex fields to load. The package optionally propagates metadata from schemas down to tables, views, and materialized views.

The Dataplex Custom Metadata Enricher focuses specifically on data quality results. It retrieves scan outputs from the BigQuery table where Dataplex publishes quality metrics and attaches scores, rule statistics, and pass/fail indicators as custom metadata on the relevant BigQuery columns in Atlan.

Both packages run on configurable schedules, keeping Atlan’s BigQuery metadata synchronized with the latest Dataplex scans and classifications.

Step 4: Cross-Platform Lineage Stitching

Once Dataplex metadata flows into Atlan, the platform stitches it with lineage from other connected sources. Atlan’s lineage engine combines BigQuery’s native lineage (captured by Dataplex) with transformation logic from dbt and consumption patterns from BI tools to build the complete end-to-end view.

This integration model preserves Dataplex as the authoritative source for GCP-native metadata while extending visibility into the broader data ecosystem. Teams benefit from both platforms’ strengths without maintaining duplicate governance definitions or manual synchronization.

Who Needs Both Dataplex and Atlan?

Organizations running Google Cloud infrastructure benefit from Dataplex’s native integration with BigQuery and other GCP services. Those with any of these conditions should evaluate adding Atlan as the enterprise metadata layer:

1. Multi-Cloud or Hybrid Data Estates

Teams operating across GCP, AWS, and Azure require unified governance that no single cloud provider’s native tool delivers. When your data warehouse sits in BigQuery but your CRM runs in Salesforce and your on-premise Oracle databases still serve critical applications, Dataplex shows only the BigQuery segment of the story.

CME Group faced exactly this challenge. Operating one of the world’s largest derivatives exchanges, they needed complete enterprise-wide lineage across cloud and legacy systems. Dataplex provided strong coverage for their Google Cloud assets but left gaps in visibility to operational systems and trading platforms.

Limitations of GCP's Dataplex that led CME Group to Atlan

Source: Atlan

After implementing Atlan, they reduced implementation cycles from weeks to days while maintaining compliance across their complex data flows.

Why CME Group chose Atlan to extend the GCP Dataplex capabilities

Source: Atlan

Modern Data Stack With Transformation Layers

Organizations using dbt to transform warehouse data encounter a visibility gap in Dataplex. While BigQuery query jobs create lineage between tables, that lineage doesn’t show dbt model names, documentation, or transformation logic. Analysts see that table_b depends on table_a but not that this relationship represents the customer_lifetime_value model with specific business rules documented in dbt.

Atlan parses dbt’s manifest.json files to surface model-level lineage with full context. Users searching for “customer lifetime value” find both the dbt model definition and the downstream Tableau dashboard it feeds, all connected through column-level lineage.

BI-Heavy Workloads Requiring Business User Access

Dataplex’s technical interface serves data engineers well but creates friction for business analysts and executives. Atlan’s business-friendly search and collaborative features address this directly. The same metadata lives in Dataplex, but Atlan’s presentation makes it accessible to non-technical stakeholders.

AI and ML Initiatives Requiring Governed Data

Machine learning teams building models on BigQuery data need to understand data lineage for feature engineering and track model dependencies for AI governance. Dataplex catalogs Vertex AI assets but doesn’t connect them to the full chain of data transformations feeding model features.

Atlan’s AI lineage capabilities trace individual features back through transformation layers to source systems, enabling teams to debug model performance issues and ensure compliance for AI deployments. The platform’s MCP server lets AI agents consume this lineage metadata directly for intelligent operations.

Organizations Outgrowing Basic Cataloging

As data maturity increases, teams need data products with SLAs, access request workflows, and custom compliance reporting. These governance patterns require a programmable metadata platform. Atlan’s app framework enables teams to build custom workflows on top of the unified metadata layer while Dataplex continues serving as the authoritative source for GCP technical metadata.

See why Atlan is a Leader in the 2026 Gartner Magic Quadrant for Data & Analytics Governance.

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Dataplex Universal Catalog + Atlan: What Does a Typical Implementation Look Like?

Most organizations follow a phased approach that establishes Dataplex coverage first and then layers Atlan on top for cross-platform governance.

Phase 1: Enable Dataplex for GCP Assets (1-2 Weeks)

Teams start by activating Dataplex APIs for their GCP projects and defining aspect types for business classifications. They configure data quality scans for critical BigQuery tables and enable lineage capture for analytics workloads.

This phase focuses on getting clean technical metadata flowing from GCP services into Dataplex Universal Catalog. Organizations define their taxonomy of aspects, document what each classification means, and establish governance roles for who can create or modify aspect definitions.

Phase 2: Deploy Atlan and Connect Non-GCP Sources (2-4 Weeks)

With Dataplex providing strong GCP coverage, teams deploy Atlan and configure connectors for their BI tools, transformation engines, and any data sources outside Google Cloud. This typically includes Tableau or Looker dashboards, dbt projects, and connections to Snowflake, Salesforce, or on-premise databases depending on the data architecture.

Atlan automatically discovers assets and builds lineage as soon as connectors authenticate. Teams see cross-platform data flows emerge within days without manual mapping.

Phase 3: Integrate Dataplex Metadata Into Atlan (1 Week)

The Dataplex connector configuration takes hours. Teams create the service account with appropriate permissions, configure the connector in Atlan, and run an initial crawl. Aspect metadata from Dataplex becomes searchable in Atlan’s unified interface immediately.

For organizations wanting deeper BigQuery integration, deploying the Dataplex Enricher and Custom Metadata Enricher packages adds 1-2 days for configuration and testing. These packages pull aspect field values and data quality results directly onto BigQuery asset pages in Atlan.

Phase 4: Establish Unified Governance Workflows (Ongoing)

With technical integration complete, teams shift to governance process design. They define data products that span GCP and non-GCP sources, establish glossary terms that apply consistently across platforms, and configure access request workflows that route appropriately whether data lives in BigQuery, Snowflake, or elsewhere.

This phase involves training different user personas. Data engineers learn to leverage cross-platform lineage for impact analysis. Business analysts adopt Atlan’s search interface for data discovery. Data governance teams define policies that enforce consistently across all connected systems.

Maintenance and Evolution

Once operational, the integration requires minimal maintenance. Dataplex continues ingesting GCP metadata automatically while Atlan connectors sync on configured schedules. Teams typically review aspect definitions quarterly to ensure they still meet business needs and audit unused metadata to keep the catalog lean.

As new data sources enter the ecosystem, teams simply configure additional connectors in Atlan. The unified metadata layer expands to accommodate new systems without disrupting existing governance processes or requiring changes to Dataplex configuration.

How Atlan’s Architecture Supports Google Cloud Deployments

Organizations committed to Google Cloud infrastructure can deploy Atlan entirely within their GCP environment. Atlan’s cloud-agnostic architecture runs on major cloud platforms including GCP, AWS, and Azure, allowing teams to host the metadata control plane wherever their data resides.

For GCP deployments, Atlan uses Google Kubernetes Engine for container orchestration and Cloud SQL for metadata storage. This keeps all data within Google Cloud’s infrastructure and supports compliance requirements for data residency.

The platform integrates naturally with GCP’s identity and access management through service account authentication. Teams leverage existing Google Cloud IAM policies rather than maintaining separate identity systems. Single sign-on through Google Workspace works seamlessly for end-user access.

Networking configurations support both public internet access and private connectivity through VPC peering or Cloud Interconnect. Organizations with strict security requirements can deploy Atlan in a private subnet with no public endpoints, accessing the interface only through approved network paths.

Atlan’s Dataplex connector operates entirely through Google Cloud APIs using least-privilege service accounts. No data moves outside the GCP project boundary during metadata extraction. The connector reads Dataplex catalog entries and aspects through standard GCP client libraries, maintaining Google Cloud’s security and audit posture.

For teams standardizing on BigQuery as their analytics warehouse, Atlan treats it as a first-class citizen. The BigQuery connector captures comprehensive metadata including datasets, tables, views, materialized views, stored procedures, and query history. Column-level lineage traces dependencies across BigQuery resources and extends to upstream ingestion tools and downstream BI dashboards.

The Metadata Lakehouse underlying Atlan stores all metadata in BigQuery itself for GCP deployments. This enables teams to query their metadata using familiar SQL tools and existing BigQuery analytics workflows. Custom compliance reports, usage dashboards, and governance metrics become straightforward SQL queries against metadata tables.

Real Stories From Real Customers: Unified Governance in Action

CME Group: Enterprise-Wide Lineage Beyond Dataplex

"With Google Dataplex, lineage only showed part of the story. Our business operates across many systems and we needed complete, enterprise-wide lineage. Atlan's platform was more intuitive, delivered on complex end-to-end lineage, and had a strong library of connectors. We also used OpenLineage for Spark jobs to tie operational lineage to our data platform."

Kiran Panja, Managing Director

CME Group

CME Group extended data lineage with Atlan

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Frequently Asked Questions

Does Atlan replace Dataplex or do they work together?

Atlan and Dataplex work together in a complementary architecture. Dataplex remains the authoritative metadata source for GCP assets, while Atlan ingests Dataplex metadata and extends governance across non-GCP systems as the enterprise-wide metadata control plane.

How does lineage work between Dataplex and Atlan?

Dataplex captures lineage automatically for BigQuery query jobs within GCP. Atlan ingests this lineage and stitches it with column-level dependencies from dbt, Snowflake, Tableau, and other connected systems to show complete end-to-end lineage from source to BI dashboards.

Can I use Atlan if my data warehouse is BigQuery?

Yes. Atlan provides native BigQuery integration regardless of whether you use Dataplex. Organizations can use Atlan with BigQuery alone or enhance the setup by also ingesting Dataplex aspects and data quality results.

What happens to Dataplex aspects when they sync to Atlan?

Dataplex aspects become searchable metadata fields in Atlan’s unified catalog. Users can filter asset searches by aspect name, key, or value. The Dataplex Enricher package optionally pulls aspect field values directly onto BigQuery assets in Atlan.

Do I need special GCP permissions to connect Atlan to Dataplex?

Yes. The integration requires creating a custom IAM role with specific Dataplex permissions including dataplex.entries.list, dataplex.entries.get, dataplex.aspectTypes.list, and dataplex.aspectTypes.get. The connector operates with read-only access and does not modify Dataplex metadata.

How much does the Dataplex-Atlan integration cost?

Dataplex follows Google Cloud’s pay-as-you-go pricing model. Atlan pricing operates separately with enterprise contracts. The integration itself requires no additional fees beyond configuring the standard Dataplex connector and optional Solutions packages included in Atlan subscriptions.

Why Dataplex and Atlan Are Better Together

Dataplex Universal Catalog and Atlan are complementary layers in a governance architecture designed for how enterprise data actually moves.

Dataplex is the right foundation for GCP-native governance. It handles BigQuery lineage, data quality scanning, IAM-based access control, and metadata management for Google Cloud assets automatically and at low operational overhead. For organizations running exclusively on GCP, it is a strong and cost-effective starting point.

The gap emerges when data crosses the GCP boundary. Atlan addresses precisely those gaps, not by replacing Dataplex but by sitting above it as the enterprise metadata context plane. Dataplex continues governing GCP assets natively. Atlan ingests that metadata, stitches it with context from every other system in the stack, enriches it with business meaning, and activates it for human teams and AI agents through a single governed layer the enterprise owns.

Organizations implementing both platforms reduce time-to-insight, improve data quality through automated governance, and enable AI initiatives with comprehensive metadata context. The approach scales as data ecosystems grow more complex while maintaining consistent governance regardless of where data lives.

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