AWS Glue Data Catalog + Atlan: A Comprehensive Guide

Transform your AWS data landscape from technical inventory to collaborative intelligence

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AWS Glue Data Catalog provides essential metadata infrastructure for your AWS data services. But as your data ecosystem grows beyond AWS—and as non-technical teams need to discover and trust data—you need capabilities that extend governance, enable collaboration, and serve diverse personas across your organization.

Atlan complements AWS Glue Data Catalog by adding an intelligent collaboration layer that connects technical metadata with business context, automates governance workflows, and makes data discoverable for everyone from data engineers to business analysts.

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Atlan adds capabilities beyond AWS Glue Data Catalog

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While AWS Glue Data Catalog excels at technical metadata management for AWS services, Atlan extends these capabilities for enterprise-wide data governance and collaboration:

🔍 Discovery for all personas

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AWS Glue provides technical interfaces for data engineers. Atlan adds Google-like search, business glossaries, and persona-based views that make data discoverable for analysts, data scientists, and business stakeholders—not just engineers.

🔗 End-to-end lineage across your stack

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AWS Glue tracks lineage within AWS services like Athena, Redshift, and EMR. Atlan captures automated column-level lineage across your entire data estate—from source systems through transformation layers to BI tools—showing complete data journeys beyond AWS boundaries.

👥 Embedded collaboration at scale

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AWS Glue focuses on metadata storage. Atlan embeds collaboration directly into the catalog with Slack/Teams integration, @mentions, rich documentation, and discussion threads—turning metadata into living knowledge that teams actually use.

🎯 Cross-system governance orchestration

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AWS Glue supports Lake Formation policies for AWS resources. Atlan orchestrates governance policies, quality rules, and stewardship workflows across AWS, Snowflake, Databricks, and 150+ other platforms—providing unified governance regardless of where data lives.

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AWS Glue Data Catalog + Atlan: How they work together for modern data governance

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Capability	AWS Glue Data Catalog	Atlan
Metadata Discovery	Automated crawlers for S3, JDBC sources, Hive-compatible metastore	AI-powered automated discovery across 150+ connectors with ML-based metadata enrichment
Search & Discovery	Technical catalog search, Hive metastore queries	Google-like search with natural language, fuzzy matching, filters, saved searches, persona-based views
Data Lineage	Column-level lineage for AWS Glue ETL jobs, Athena queries, Redshift Spectrum	Automated end-to-end column-level lineage across AWS + Snowflake + Databricks + dbt + BI tools with impact analysis
User Interface	AWS Console (technical), API/SDK access	Multi-persona UI: technical workspace, business glossary, marketplace, embedded Slack/Teams
Collaboration	Limited to IAM permissions and tagging	Native @mentions, discussions, Slack/Teams integration, rich documentation, announcements
Business Glossary	Tag-based custom metadata fields	Full business glossary with terms, relationships, hierarchies, embedded in search and lineage
Data Quality	AWS Glue Data Quality rules (DQ), CloudWatch metrics	No-code quality rules + bi-directional integration with dbt tests, Great Expectations, Soda, Monte Carlo
Governance Policies	Lake Formation permissions, tag-based access control	Policy Center for unified governance across platforms with automated enforcement and stewardship workflows
Integration Breadth	Native AWS services (Athena, Redshift, EMR, Lake Formation, SageMaker)	150+ connectors: AWS + Snowflake + Databricks + dbt + Looker + Tableau + Fivetran + Airflow + Kafka + more
Metadata Sync	Within AWS ecosystem	Bi-directional sync with AWS Glue, enabling metadata from Atlan to flow back to Glue

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Why Atlan is the best universal catalog for AWS environments

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1. Complements rather than replaces AWS Glue

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Atlan doesn’t compete with AWS Glue Data Catalog—it extends it. Organizations continue using Glue as their technical metastore for AWS services while adding Atlan as a collaboration and governance layer. Atlan’s native AWS integrations connect with S3, Glue, Athena, Redshift, EMR, MSK, QuickSight, and SageMaker Unified Studio, pulling metadata into a unified view while maintaining Glue as the source of truth for AWS-native operations.

The architecture is complementary: AWS Glue handles technical catalog functions like schema management and integration with AWS query engines, while Atlan provides the human layer—making metadata discoverable, governable, and actionable for diverse teams.

2. True end-to-end lineage, not just AWS lineage

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Modern data architectures rarely exist entirely within one cloud. AWS Glue excels at tracking transformations within AWS—following data from S3 through Glue ETL to Athena or Redshift. But what happens when that data originated in Salesforce, was transformed by dbt running on Snowflake, joined with data from your on-premises Oracle warehouse, and visualized in Tableau?

Atlan automatically captures column-level lineage across this entire journey. When an analyst in Tableau questions a metric, they can trace it back through transformation layers spanning multiple platforms to understand exactly where each value originated. Organizations report 60% faster root cause analysis when they can see complete data journeys, not just platform-specific segments.

3. Metadata that teams actually use

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AWS Glue’s technical focus serves data engineers well, but most organizations need data catalogs that serve analysts, data scientists, product managers, and business stakeholders—not just engineers. Atlan provides persona-based interfaces where each user sees relevant views: analysts see certified datasets and business context, engineers see technical lineage and quality metrics, business users see glossary terms and data products.

The embedded collaboration features transform metadata from static documentation into living knowledge. Teams discuss data quality issues directly on asset pages, @mention data owners for clarification, receive Slack notifications when datasets change, and document tribal knowledge where it’s most useful—right next to the data itself.

4. Governance that scales across your entire stack

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AWS Lake Formation provides robust access controls for AWS resources, but modern enterprises need governance that works consistently whether data lives in AWS, Snowflake, Databricks, or elsewhere. Atlan’s Policy Center lets you define governance policies once and enforce them across platforms. When PII data is detected in S3, the same classification, access controls, and masking policies apply when that data flows through Glue transformations into Redshift and beyond.

Stewardship workflows automate governance tasks: new datasets trigger ownership assignment, quality failures create Jira tickets, certification expiration sends Slack reminders to data owners. This automation reduces manual coordination overhead by 75% according to customer reports.

5. AI-powered automation reduces manual metadata work

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While AWS Glue crawlers automate schema discovery, Atlan’s AI capabilities go further. Machine learning models analyze query patterns to automatically suggest relevant tags, identify PII and sensitive data with high accuracy, recommend similar assets when analysts search, and propagate metadata changes intelligently across dependent assets.

Programmatic bots handle routine metadata management: tagging assets based on naming conventions, propagating business glossary terms through lineage, flagging orphaned datasets with no recent usage, and surfacing assets that might contain sensitive data based on column names and content patterns.

6. Built for the modern data stack reality

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AWS-centric organizations rarely stay AWS-only. Most enterprises adopt Snowflake for certain workloads, use Databricks for machine learning, run dbt for transformation logic, and rely on tools like Fivetran for data ingestion. AWS Glue Data Catalog wasn’t designed for this heterogeneous reality.

Atlan embraces it. The platform treats AWS Glue as one important component of a broader ecosystem, providing unified discovery, lineage, and governance across all platforms. Teams get a single source of truth for data assets regardless of where they live, while AWS Glue continues serving its essential role as the technical metastore for AWS query engines.

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Real stories from real customers using Atlan with AWS

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Build a future-ready data estate with Atlan and AWS

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AWS Glue Data Catalog provides essential technical infrastructure for AWS data services. Atlan extends this foundation with enterprise-grade collaboration, cross-system governance, and AI-powered automation that make metadata accessible and actionable for your entire organization.

The combination gives you the best of both worlds: AWS Glue’s deep integration with AWS services and performance optimization for query engines, plus Atlan’s human-centered design, cross-platform governance, and intelligent automation that scale with organizational complexity.

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FAQs about AWS Glue Data Catalog and Atlan

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1. Does Atlan replace AWS Glue Data Catalog?

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No. Atlan complements AWS Glue Data Catalog rather than replacing it. AWS Glue continues serving as the technical metastore for AWS services like Athena, Redshift Spectrum, and EMR. Atlan connects to Glue through native integrations, pulling metadata into a unified collaboration layer while maintaining Glue as the source of truth for AWS-native operations. Organizations use both together: Glue for technical AWS catalog functions, Atlan for human-centered discovery and cross-system governance.

2. How does Atlan integrate with AWS Glue?

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Atlan connects to AWS Glue through native integrations that catalog Glue databases, tables, jobs, and crawlers. The integration captures technical metadata from Glue and enriches it with business context, collaboration features, and automated lineage that extends beyond AWS. Setup requires IAM policies and authentication configuration through user-based access or role delegation. Atlan supports bi-directional metadata sync, allowing certain metadata from Atlan to flow back to Glue when needed.

3. Can Atlan capture lineage from AWS Glue transformations?

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Yes. Atlan automatically captures column-level lineage from AWS Glue ETL jobs, crawlers, and transformations. The lineage extends beyond Glue to show complete data journeys: from source systems through S3, Glue transformations, Athena queries, Redshift processing, and final BI visualizations. This end-to-end visibility helps teams understand data flow across their entire stack, not just within AWS boundaries.

4. How does Atlan’s pricing compare to AWS Glue costs?

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AWS Glue uses pay-per-use pricing based on crawler runtime, ETL job DPU-hours, and catalog storage/requests. Atlan uses annual subscription pricing based on platform size and user count. For enterprises already invested in AWS Glue, Atlan adds a collaboration and governance layer with predictable costs. The value comes from improved data discovery efficiency, reduced manual governance overhead, and broader organizational adoption—benefits that often justify the investment through productivity gains.

5. What AWS services does Atlan integrate with beyond Glue?

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Atlan integrates natively with S3, AWS Glue, Athena, Redshift, EMR, MSK (Kafka), QuickSight, and SageMaker Unified Studio. The platform also connects with AWS Lake Formation for governance metadata and supports AWS services through standard JDBC/API connections. This breadth lets organizations catalog their entire AWS data estate within Atlan while maintaining native AWS service integrations for technical operations.

6. How does Atlan handle AWS Glue Data Quality rules?

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Atlan complements AWS Glue Data Quality by providing a unified view of quality metrics across platforms. While Glue Data Quality monitors AWS datasets, Atlan aggregates quality metrics from Glue DQ alongside checks from dbt, Great Expectations, Soda, and Monte Carlo. Data consumers see quality status regardless of which tool runs the checks, and quality failures can trigger automated workflows like notifying data owners or creating Jira tickets.

7. Can business users discover AWS data without learning Glue’s technical interface?

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Yes. This is one of Atlan’s core value propositions. AWS Glue’s interface is designed for data engineers managing technical catalog operations. Atlan provides persona-based views where business users can search for datasets using natural language, browse curated data products by business domain, and understand data through business glossary terms and rich documentation—without needing to understand Glue’s technical structure.

8. How does Atlan’s approach to metadata differ from AWS Glue’s?

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AWS Glue treats metadata as technical infrastructure supporting query engines and ETL jobs. Atlan treats metadata as organizational knowledge requiring collaboration, curation, and accessibility for diverse personas. Glue excels at schema management and AWS service integration. Atlan excels at making that technical metadata discoverable, governable, and actionable through human-centered features like embedded discussions, automated lineage visualization, and no-code governance workflows. The approaches are complementary, not competing.

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