DataHub vs. Atlan
Enterprise context management built on a trusted open-source foundation
Why do some teams choose DataHub over Atlan?
What real data teams found when they put DataHub and Atlan head to head.
01 OPEN SOURCE
Built on a flexible open source foundation
DataHub Cloud is built on the open source DataHub project under Apache 2.0. Self-host or take the managed cloud, and the metadata model stays the same. Atlan is closed commercial software, so your graph lives inside the billing relationship.
02 LINEAGE
Leads the industry in cross-platform data lineage
DataHub’s cross-platform data lineage was built over a decade with 16,000+ contributors and runs in production at enterprises with 2M+ assets, including Apple, Netflix, and Visa. Column-level lineage propagates through the graph over Kafka in near real time, and context quality follows lineage quality.
03 EVENT-DRIVEN
Pushes every metadata change to every system that needs it, instantly
DataHub publishes every metadata change to Kafka. Data pipelines, alerting tools, and governance workflows subscribe directly and consume independently. Atlan sends events through webhooks, a separate HTTP call per consumer. Each one is another integration to maintain and delivery slows under load.
04 SECURE INGESTION
Reaches your firewalled sources without new infrastructure
DataHub’s Remote Executor runs inside your VPC with minimal overhead and no inbound connectivity required to securely ingest metadata from on-premises and firewalled sources. Atlan’s Secure Agent covers private environments too, but it requires adding Kubernetes infrastructure overhead.
05 AGENT ACCURACY
Turns agent guesses into trusted answers you can act on
Context Intelligence doubled analytics agent accuracy on a real enterprise benchmark. Miro went from ~50% to ~90% on benchmark questions after adding DataHub as the context layer, driven by query history, cross-source context, and expert-validated business meaning.
06 DATA QUALITY
Catches the break wherever it happens, and everywhere after
DataHub monitors a broader set of platforms, traces the blast radius through lineage the moment a check fires, and notifies every downstream owner automatically. Atlan’s native checks cover only Snowflake, Databricks, and BigQuery.
Trusted by data teams everywhere
“Now, our data teams can discover what they need, understand the full impact of changes before they make them, and self-serve governance questions that used to require deep legacy knowledge. It’s transformed how we operate at scale, and it’s positioning us perfectly for the agentic AI future we’re building toward.”
Nitin Sarma
Sr. Engineering Manager, Data Discovery, Governance & Experiences
“It’s nice to see teams starting a DataHub-first approach to data. When they’re discussing something, it’s ‘have you looked in DataHub?’ rather than just asking questions. It’s become a key part of the data ecosystem, and that’s true going forward for AI and for agents.”
Hugo Hobson
Senior Data Engineer
Where DataHub goes further
Book a demoKey capabilities in DataHub that make a difference in production
Skips the crawl and keeps context current
DataHub’s Kafka-native architecture propagates metadata changes across your estate in near real time, with no scheduled crawl to wait for. When a schema, an ownership record, or a quality signal changes upstream, every downstream system that depends on it knows immediately.
Grounds every answer in your SQL history
Before an agent writes SQL, DataHub retrieves the joins, filters, and metric formulas your analysts have run for years. Domain experts review that context, and evals test it against real business questions.
Traces a wrong answer back to the context that caused it, then proves the fix
Evals run against the questions your business actually asks. When an agent fails, DataHub names the specific definition, document, or query pattern it relied on. You fix the context, re-run the eval, and confirm the answer is correct. Context quality compounds with every correction instead of drifting between manual review cycles.
Updates context on its own as your stack drifts
DataHub’s Context Curation Agent monitors audit logs, BI dashboards, and schema changes continuously. When something changes, like a schema update, a deprecated metric, or a new pipeline, context updates without anyone scheduling a new generation run. Drift is caught before an agent inherits it.
Brings the decision to your experts, wherever they work
When DataHub’s Context Curation Agent surfaces a conflicting definition or an ambiguous metric, a structured Decision routes to the right expert via Model Context Protocol (MCP), inside Claude, Cursor, Slack, or wherever they already work. One interaction approves, rejects, or refines it. The validated context flows immediately to every downstream agent.
PRODUCT PERFORMANCE
Skips the crawl and keeps context current
PRODUCT PERFORMANCE
Skips the crawl and keeps context current
DataHub’s Kafka-native architecture propagates metadata changes across your estate in near real time, with no scheduled crawl to wait for. When a schema, an ownership record, or a quality signal changes upstream, every downstream system that depends on it knows immediately.
CONTEXT INTELLIGENCE
Grounds every answer in your SQL history
CONTEXT INTELLIGENCE
Grounds every answer in your SQL history
Before an agent writes SQL, DataHub retrieves the joins, filters, and metric formulas your analysts have run for years. Domain experts review that context, and evals test it against real business questions.
CONTEXT IMPROVEMENT
Traces a wrong answer back to the context that caused it, then proves the fix
CONTEXT IMPROVEMENT
Traces a wrong answer back to the context that caused it, then proves the fix
Evals run against the questions your business actually asks. When an agent fails, DataHub names the specific definition, document, or query pattern it relied on. You fix the context, re-run the eval, and confirm the answer is correct. Context quality compounds with every correction instead of drifting between manual review cycles.
CONTEXT MAINTENANCE
Updates context on its own as your stack drifts
CONTEXT MAINTENANCE
Updates context on its own as your stack drifts
DataHub’s Context Curation Agent monitors audit logs, BI dashboards, and schema changes continuously. When something changes, like a schema update, a deprecated metric, or a new pipeline, context updates without anyone scheduling a new generation run. Drift is caught before an agent inherits it.
CONTEXT VALIDATION
Brings the decision to your experts, wherever they work
CONTEXT VALIDATION
Brings the decision to your experts, wherever they work
When DataHub’s Context Curation Agent surfaces a conflicting definition or an ambiguous metric, a structured Decision routes to the right expert via Model Context Protocol (MCP), inside Claude, Cursor, Slack, or wherever they already work. One interaction approves, rejects, or refines it. The validated context flows immediately to every downstream agent.
Compare key features:
DataHub vs. Atlan
Book a demoDataHub
Atlan
Open-source foundation
Kafka event stream for real-time, scalable metadata propagation
Firewalled ingestion without a Kubernetes cluster
Data quality monitoring across your full stack
Failed eval traced to a context fix
Expert context approval inside MCP-connected tools
Getting started
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FAQs
What is Atlan?
What is Atlan?
Atlan is a proprietary, AI-native data catalog that connects modern stacks like Snowflake, Databricks, dbt, and Tableau into a unified metadata graph, positioned increasingly as a context layer that teams and agents query as a shared source of truth.
What is DataHub?
What is DataHub?
DataHub is the enterprise context platform built for data and AI teams at scale. It started as the open-source metadata platform behind LinkedIn’s internal data ecosystem and is now in production in thousands of organizations, including Netflix, Apple, Visa, and CVS Health. DataHub has the largest open-source community of data and AI practitioners with over 16,000 active members and more than 3 million monthly downloads.
DataHub Cloud is the managed version of DataHub Core, DataHub’s Apache 2.0 open-source data catalog, with additional observability, context management, and automation features included. The platform ships new releases monthly and supports over 140 connectors.
How does Atlan’s architecture compare to DataHub’s architecture?
How does Atlan’s architecture compare to DataHub’s architecture?
DataHub is Kafka-native. Every metadata change becomes an event, and pipelines, alerting tools, and data governance workflows subscribe directly and consume independently. Adding a tenth consumer has no impact on performance, requires no reconfiguration, and introduces no new failure point. Atlan delivers metadata events to external systems through webhooks, a separate HTTP call per consumer, so each consumer is another integration to maintain and delivery becomes the platform’s problem under load.
Can I self-host either platform?
Can I self-host either platform?
DataHub Core is Apache 2.0, so you can self-host it, extend it through APIs and SDKs, and keep governance logic in the toolchain your engineers already run. Atlan is a closed commercial platform with no open source offering.
Can I manage DataHub as code with GitOps?
Can I manage DataHub as code with GitOps?
DataHub is API-first. Governance logic lives in code, in GitLab, APIs, and SDKs, so the definitions and policies your team maintains sit in version control alongside everything else you ship. Full API and SDK access means the UI is one way in, not the only way.
Is DataHub or Atlan better for large-scale enterprise deployments?
Is DataHub or Atlan better for large-scale enterprise deployments?
DataHub is the better fit for large, complex estates. Its cross-platform lineage was built over a decade with 16,000+ contributors and is validated in production at enterprises running 2M+ data assets, including Apple, Netflix, Visa, and CVS Health. Metadata changes propagate over Kafka, so any number of downstream systems stay current from one stream.
Atlan relies on point-to-point webhooks, and teams that evaluated it reported crawl times that capped how frequently quality checks could run. DataHub also ingests metadata from behind the firewall without requiring a Kubernetes cluster.
How does DataHub keep agent context from drifting?
How does DataHub keep agent context from drifting?
DataHub’s Context Curation Agent monitors audit logs, BI dashboards, and schema changes, detects drift, and updates context on its own. Anything needing human judgment surfaces as a structured Decision to the right expert.