OpenMetadata vs Apache Atlas: which to choose

OpenMetadata vs Apache Atlas: when each one fits

Choose Apache Atlas when your estate is still built around Hadoop and you need its governance services to stay where they are. Atlas documents hooks for Hive, HBase, Kafka, Sqoop, Storm, Falcon and Couchbase, and its README says metadata veracity is maintained with Apache Ranger, with security that is both role based and attribute based. Choose OpenMetadata when most of your data sits in warehouses, BI tools and modern pipelines, when you want governance work such as glossaries, classifications and domains handled through a UI, and when you do not want to operate HBase and Solr. OpenMetadata lists more than 130 connectors and is released under Apache-2.0, maintained by Collate.

The decision turns on three things. First, how much of the estate is still Hadoop. If Hive and Kafka are the centre of your lineage, Atlas's existing hooks carry real value, and a replacement must match every hook you depend on. If Hadoop is a shrinking share, the cost of keeping Atlas's hook coverage alive grows relative to the warehouse and BI sources you add. Second, release cadence. Atlas's downloads page lists 2.5.0 on April 30, 2026, after 2.4.0 on January 2, 2025, so confirm the current maintenance plan with the Atlas community before you commit. OpenMetadata's 2.0 line has shipped maintenance releases roughly every one to two weeks since 2.0.0 on August 24, 2026, so check its release notes for breaking changes before each upgrade.

Third, your team's skills. Atlas persists metadata in JanusGraph with HBase as the storage backend and Solr for indexing in the documented setups. Operating that stack well is a specialist skill, and many teams that inherited it no longer have it. OpenMetadata's bare metal prerequisites are Java 21, MySQL 8.0.42 or later or PostgreSQL 15 or later, and Elasticsearch 9.x or OpenSearch 3.x, with Airflow running ingestion. Those components are common in most platform teams, but they are still a new operating surface. Ask each vendor or your DeployOpen contact to map your current Atlas hooks to OpenMetadata connectors, then run a test ingestion against a copy of one Hive database before deciding.

Which catalog fits which scenario

This table maps common estates to the catalog that usually fits better, with the condition under which each fit holds. Read each row against your own sources, Hadoop footprint and access control model, because the right answer depends on what must keep running during the move.

Scenario	Better fit	Why and under what condition
Estate still centered on Hadoop, with Hive and Kafka hooks	Apache Atlas (short term)	Atlas ships Hive and Kafka hooks and runs on the HBase and Solr stack your team may already operate. This holds only while Hadoop operations skills and those components stay in place.
Warehouse and BI estate with many non-Hadoop sources	OpenMetadata	OpenMetadata lists more than 130 connectors for warehouses, BI tools, pipelines and ML platforms. Confirm that your specific sources are covered before choosing.
Team that depends on Ranger for runtime access control	Apache Atlas (confirm integration needs)	The Atlas README describes Ranger integration for runtime access control. OpenMetadata's security documentation should be checked for equivalent controls before any move.
Team with no Hadoop operations skills	OpenMetadata	OpenMetadata's bare metal path needs Java 21, a MySQL or PostgreSQL database and Elasticsearch or OpenSearch, with Airflow for ingestion. Staff who already run those services will find this easier than running HBase and JanusGraph.
Team that must keep existing Atlas hooks running during transition	Apache Atlas (during cutover)	Running Atlas and OpenMetadata side by side lets Hive and Kafka hooks keep feeding Atlas until consumers move. Plan the retirement date with the owners of each hook.

What each stack means for day-two operations

Both platforms ask a team to run several stateful services, not one application. The cards below separate the Atlas graph, storage and messaging layers from the OpenMetadata server, database, search and ingestion layers, and name the skills each requires on call.

Atlas type system and entities

Atlas models metadata as types and entities, covering technical and business metadata and the relationships between them. Day-two work here is mostly about type definitions: changing a type or loading a bulk import can affect every hook and consumer that depends on it. Before a migration, inventory the custom types your teams have added and test how they load into the target catalog. Operators need people who understand the Atlas model, not just the service.

JanusGraph, HBase and Solr layers

In the documented setups, Atlas stores metadata in a JanusGraph graph repository on Apache HBase and indexes it with Apache Solr. Each layer has its own upgrade path, sizing, failure modes and logs, so an incident may begin in HBase or Solr while the symptom appears in Atlas. Monitoring must cover all three, and your on-call rotation needs HBase and Solr administration skills. Confirm with your Hadoop distribution vendor which versions of these components you can run together.

Atlas REST and Kafka interfaces

Atlas exposes its functions through a REST API and a Kafka-based messaging interface, where hooks publish metadata and consumers read change events. Day-two risk sits in the topics and consumer groups: a lagging consumer or a changed message format can silently stop downstream updates. Monitor consumer lag and keep an inventory of every integration calling the REST API, because each one must be re-pointed during a cutover.

OpenMetadata server, database and search

The OpenMetadata server runs on Java 21 and needs MySQL 8.0.42 or later, or PostgreSQL 15 or later, plus Elasticsearch 9.x or OpenSearch 3.x for search. Minimum sizing is 2 vCPUs, 4 GB RAM and 20 GB storage for the server, and at least 2 vCPUs, 2 GB RAM and 30 GB storage each for the database and search services. Plan patching and version upgrades across all three, and confirm these minimums against your expected catalog volume before you commit to them.

Airflow-based ingestion operations

OpenMetadata runs metadata ingestion through Airflow, so your team must operate a scheduler and its DAGs in addition to the catalog itself. Ingestion failures, credential rotation for source systems and Airflow version alignment become recurring tasks. Confirm which Airflow version your target OpenMetadata release expects, since Airflow 2 has reached end of life and the 2.0 line of OpenMetadata moved ingestion to Airflow 3.3.0.

Upgrade and on-call skills

An Atlas team typically needs Hadoop operators who know HBase, Solr, Kafka and JanusGraph. An OpenMetadata team needs Java and database administrators, search cluster operators and Airflow engineers. Both stacks benefit from a named owner for each layer during incidents. Ask each vendor or community for its supported upgrade paths and breaking-change notes before you fix a date for the move.

How OpenMetadata and Apache Atlas compare on capability

This table sets OpenMetadata's modern catalog stack against Apache Atlas's Hadoop-era architecture across licensing, release activity, infrastructure, integrations and governance. Read each row with the condition that makes it matter for your estate, and confirm the details marked for verification against current documentation before you commit.

Capability	OpenMetadata	Apache Atlas
License	Apache License 2.0, maintained by Collate, which also sells commercial enterprise capabilities and premium support	Apache-2.0, an Apache Software Foundation project that describes itself as governance services for Hadoop
Current release line and recent releases	2.0 line: 2.0.0 on August 24, 2026, with maintenance releases through 2.0.5 on October 8, 2026	2.5.0 on April 30, 2026, 2.4.0 on January 2, 2025 and 2.3.0 on December 6, 2022; 2.6.0 release candidate tags are in the repository
Java requirement	Java 21 for the bare metal deployment	Builds with Java 8, 11 or 17, according to the README; confirm the supported runtime for your chosen release
Storage and search stack	MySQL 8.0.42 or later, or PostgreSQL 15 or later, plus Elasticsearch 9.x or OpenSearch 3.x for search	JanusGraph graph repository with Apache HBase as storage and Apache Solr for indexing in documented setups; BerkeleyDB is an alternative setup
Ingestion model	Connector-based ingestion, with Airflow used to run metadata ingestion jobs	Hooks push metadata from Hive, HBase, Kafka, Sqoop, Storm, Falcon and Couchbase into Atlas; confirm which hooks your release still ships
Hive, Kafka and other integrations	More than 130 connectors for warehouses, BI tools, pipelines and ML platforms	Hooks for Hive, HBase, Kafka, Sqoop, Storm, Falcon and Couchbase; confirm coverage for your warehouse and BI tools
Lineage and OpenLineage	Table, column, dashboard, pipeline and metric lineage, with OpenLineage event integration	Lineage is modeled through entities and relationships; confirm the depth of lineage captured by your hooks and whether OpenLineage is supported
Data quality and profiling	Data quality tests, profiling, freshness checks, glossaries, classifications and domains	Classifications and business metadata through the type system; confirm whether data quality and profiling features are in scope for your use case
Programmatic access	REST APIs, a Python SDK, TypeScript packages and Java libraries	All functionality exposed through a REST API, plus a Kafka-based messaging interface for hooks and change events
Ranger integration and access control	Security and access documented in the OpenMetadata security guide; confirm how your authorization model maps to your policy engine	Metadata veracity maintained with Apache Ranger, with role-based and attribute-based security; Ranger is central to the Atlas access model

How access control differs between Ranger and OpenMetadata

Atlas leaves runtime access control to Apache Ranger. The Atlas README says metadata veracity is maintained with Apache Ranger to prevent unauthorized access paths to data at runtime, and it describes Atlas security as both role based and attribute based. In practice, the question for a Hadoop estate is not whether Atlas has access controls, but where those controls are defined. Many Hadoop estates already run Ranger policies for Hive, HBase, Kafka and other services, so Atlas keeps governance and enforcement tied to a control plane the team already operates. Before planning a migration, list which Ranger policies depend on Atlas tags or classifications, because those policies will need an equivalent path after the move.

A move to OpenMetadata changes where policy is defined and enforced. OpenMetadata is a catalog with its own identity and access model, documented in its security section, so access to metadata is managed inside the catalog rather than by a Ranger plugin in front of each engine. That is a different responsibility split. Ranger enforces access at query and service time, while OpenMetadata governs who can view, edit and administer catalog objects. Whether any Ranger enforcement for data itself must remain in place is a decision for your architecture review, not something the catalog replaces by default. Teams should map each existing Ranger policy to one of three outcomes: keep it in Ranger, recreate it in the catalog, or retire it.

Confirm SSO and role mapping before you decide. Read OpenMetadata's security documentation for its current identity and access model, including how authentication and roles are configured. Then check, with your identity provider team and against your own environment, which SSO protocols your provider supports, how groups map to OpenMetadata roles, and how role changes are audited. Do not assume attribute-based rules carry over from Atlas, since that model needs to be confirmed against OpenMetadata's documentation for your release. Record the results in a control mapping before the cutover plan is approved.

What is free, what is paid and what is supported

Apache Atlas is an Apache Software Foundation project licensed under Apache-2.0. The license lets you run, modify and redistribute the code without a license fee, and the project does not offer a commercial edition in the facts available for this comparison. Atlas is a Hadoop-era governance service, so the cost of running it sits almost entirely in infrastructure and the people who operate it. Support for Atlas comes from the open-source community or from a third party you contract directly, and you should confirm which of those options your team can use before you commit.

License cost: Apache-2.0 carries no license fee. Your recurring costs are the servers, storage and staff time needed to run JanusGraph, HBase and Solr.
Paid edition: No paid Atlas edition is documented in the supplied facts. Ask any vendor that offers Atlas services to state in writing what they support and at what level.
Support path: Support is community-based or third party. Confirm response expectations and who holds the operational knowledge if an upgrade goes wrong.
Release cadence: Check the Apache Atlas downloads page for the current release and any security fixes before you plan an upgrade window.

The license model matters for procurement when your policy requires a contracted vendor with a written support obligation for every production system. In that case, confirm with a third-party provider what they will cover, or document why community support meets your control requirements.

What drives the running cost of each catalog

The running cost of Apache Atlas is driven first by its storage stack. The documented setups pair the JanusGraph graph repository with Apache HBase as the storage backend and Apache Solr for indexing, with BerkeleyDB as an alternative. Each of those services has its own nodes, capacity planning, backups and patching. OpenMetadata's bare metal prerequisites are shorter: a MySQL or PostgreSQL database, Elasticsearch 9.x or OpenSearch 3.x for search, Java 21 for the server, and Airflow to run metadata ingestion. Compare the inventory of components, not just the server count. Ask your team to list every service that must stay up, who patches it and where its backups live, and run that list against both platforms. Skills and upgrades are the second cost. Atlas runs on the Hadoop operating model, so the people who keep it healthy are usually the same people who run HBase, Solr and the Hadoop cluster. OpenMetadata shifts the work toward Java, Airflow and search operations. Upgrades carry their own cost on the OpenMetadata side: the 2.0 release notes list breaking changes such as moving embedding credentials into an llmConfiguration block, a new Databricks auth format, and rebuilding custom ingestion images for Python 3.12. Plan those changes as recurring work, and confirm the current Atlas and OpenMetadata release windows against the OpenMetadata releases page and the Apache Atlas downloads before you budget. Separate the one-time work from the recurring work. One-time costs include the migration of Atlas types and entities, replacing Hive and Kafka hooks with OpenMetadata connectors, and rebuilding Ranger-based access policies in OpenMetadata's security model. Recurring costs include hosting for each service, upgrades, and any paid support. OpenMetadata is maintained by Collate, which offers commercial enterprise capabilities and premium support, and Atlas has no vendor-backed support contract. DeployOpen does not publish prices, and no price is given here. Request a quote built from a written scope that lists each component, the hosting model, the number of ingestion jobs and hooks, and the support hours you expect. Treat the minimum sizing figures for OpenMetadata (2 vCPUs, 4 GB RAM and 20 GB storage for the server, with at least 2 vCPUs, 2 GB RAM and 30 GB storage for each of the database and search services) as floors, not a capacity plan. Real sizing depends on your asset count, profiling schedule and query load, and should be tested in your environment.

What moving from Atlas to OpenMetadata involves

The sequence moves an Atlas estate to OpenMetadata without a big-bang switch. Each step ends with a check that a catalog function works in the new system before the old one is retired. Because Atlas hooks, Ranger policies and downstream consumers are rebuilt rather than exported, the inventory at the start decides how long the move takes.

Inventory Atlas types, entities, hooks and Ranger policies
The Atlas owner and the platform team list every custom type definition, the entity counts per type, each enabled hook (Hive, HBase, Kafka, Sqoop, Storm, Falcon or Couchbase), the Kafka topics hooks and consumers use, and the Apache Ranger policies that gate access to catalog data. Atlas types and entities carry over in concept, but the exact model has to be re-expressed in OpenMetadata's entity model, so this list becomes the mapping document. It is done when every type, hook and policy has an owner and a target: a reused OpenMetadata connector, a custom ingestion job, or a retirement decision. Confirm the Atlas version in use first, since the current release line is documented in the Apache Atlas downloads page.
Stand up OpenMetadata dependencies and server
The platform team provisions the OpenMetadata server, a MySQL 8.0.42 or later or PostgreSQL 15 or later database, Elasticsearch 9.x or OpenSearch 3.x for search, and Java 21 on bare metal, or the equivalent on Kubernetes. Minimum sizing is documented as 2 vCPUs, 4 GB RAM and 20 GB storage for the server, with the database and search services each needing at least 2 vCPUs, 2 GB RAM and 30 GB storage, but production sizing depends on entity volume and must be confirmed against the deployment guide. The step is complete when the server starts, authenticates users through the chosen identity configuration, and a test entity can be created and searched. Running the Airflow instance that powers ingestion is part of this work, not a later add-on.
Rebuild ingestion for each source with connectors and Airflow
For each source in the inventory, the data team configures the matching OpenMetadata connector or writes a custom ingestion job, then schedules it through Airflow. Atlas hooks push metadata from applications, so this step replaces push with pull for most sources and requires a decision for any hook that has no connector equivalent. Before building ingestion images, the team reviews the OpenMetadata 2.0 breaking-change notes: ingestion images move to Python 3.12, custom images must be rebuilt, Databricks auth uses a new format, and Great Expectations must be migrated to 1.3 before upgrading. The step is done when each source has a scheduled job whose run output matches the entity counts recorded in step one.
Map glossaries, classifications and lineage
Business glossary terms, classification tags and lineage edges are mapped from Atlas into OpenMetadata glossaries, classifications and lineage, which support table, column, dashboard, pipeline and metric lineage through OpenLineage events. Business intent transfers well, because the terms and their owners are written down in both systems, but lineage is usually rebuilt from source connectors rather than copied, and lineage that depended on custom Atlas entities needs a manual check. Data stewards sign off on a sample of terms and lineage paths against the source systems. The step closes when stewards confirm the sample and any gaps are logged as named backlog items.
Run both catalogs in parallel and cut over consumers
Both catalogs run side by side while consumers move. Teams repoint REST API clients to OpenMetadata, using its REST APIs, Python SDK, TypeScript packages or Java libraries, and replace Atlas Kafka consumers with the event integration each team chooses. Access rules are re-created in OpenMetadata's security model and checked against the Ranger policy inventory, since Atlas access control depended on Ranger. Each consumer is cut over only after its read and write calls return the expected results in OpenMetadata. The parallel period is where most of the effort sits, because every consumer and policy has to be verified.
Retire the HBase, Solr and JanusGraph stack
Once no consumer or hook writes to Atlas and the parallel reads match, the platform team exports a final Atlas backup, stops the hooks and the Atlas service, and decommissions the JanusGraph repository, HBase storage and Solr index. Keep the export until the rollback window agreed with the business owners closes, and record where it is stored. The step is complete when the Atlas services are off, the backup is verified and the retirement is recorded in the change log. Confirm any retention or audit requirements for the Atlas metadata history with your compliance lead before deleting it.

Where Atlas-to-OpenMetadata projects usually run long

Atlas migrations rarely stall on the catalog itself. They stall on the custom types, event consumers, Ranger policies, lineage paths and identities that grew around Atlas over years. Each card names a risk, the condition that triggers it, and the inventory and evidence to collect before cutover.

Custom Atlas types without connectors

Teams that extended Atlas's type system with their own entity definitions and relationships will find that OpenMetadata's connectors map to its own entity model, not to those custom types. Export every custom typedef and count the entities that use it. For each one, decide whether it maps to an existing OpenMetadata asset type, a custom property, or a tag, and confirm that choice with a sample of real entities before you load anything at scale.

Kafka consumers tied to Atlas events

Atlas's Kafka-based interface sends hook messages and change events to consumers. Any downstream service that parses those event formats will break when Atlas is switched off, and OpenMetadata does not consume Atlas topics as a drop-in. Inventory every consumer group subscribed to Atlas topics, record the fields each one reads, and confirm with the owning teams which events they need from OpenMetadata's REST APIs, webhooks or OpenLineage integration.

Ranger policies keyed to classifications

Atlas's README describes Apache Ranger as the control that keeps metadata veracity and blocks unauthorized access paths to data at runtime. If Ranger policies reference Atlas classifications, those policies will not automatically follow the data into OpenMetadata, which has its own classifications and domains. List each policy that reads a classification, then map every classification to its OpenMetadata equivalent and test access with a non-admin account before retiring the Atlas-side policy.

Lineage gaps for uncovered pipelines

Atlas lineage often came from custom hooks written for in-house jobs, such as Sqoop, Storm or Falcon pipelines, or homegrown scripts. OpenMetadata's connectors and OpenLineage events cover many common sources, but a custom job without a connector or emitter will show no lineage. Build a list of every pipeline that feeds a governed table, mark the ones with no connector, and compare lineage graphs for a sample of tables before and after migration.

Users, groups and ownership mapping

Atlas access, ownership and stewardship typically follow Hadoop-era directory groups, while OpenMetadata assigns owners, teams and roles inside its own model and its security configuration. Export the current group memberships and steward assignments, decide which directory groups map to which OpenMetadata roles, and confirm that owners of each high-value asset are set before users are cut over. Missing owners are a common reason catalog adoption stalls after launch.

Java 21 and Airflow version checks

OpenMetadata's bare metal prerequisites include Java 21 and Airflow for running metadata ingestion, and its 2.0 release moved ingestion images to Python 3.12 with Apache Airflow 3.3.0. Atlas builds with Java 8, 11 or 17, so Java runtimes and build images on the old stack will need a version review. Check the Java runtime on every host, confirm the Airflow version your ingestion will run on, and note that Airflow 2 has reached end of life.

Questions teams ask before replacing Apache Atlas

What drives the cost and effort of moving from Apache Atlas to OpenMetadata?

The cost depends mostly on how much Atlas metadata you have and how many of its hooks and integrations you depend on. Atlas stores its type system, entities and relationships in a JanusGraph graph backed by HBase and indexed in Solr, and Hive, Kafka and other hooks feed it continuously. Each hook or custom type you must re-create in OpenMetadata adds work, as does rebuilding the lineage Atlas captured. Costs have two parts: internal staff time and any external services you buy, such as a fixed-scope engagement with DeployOpen. Ask any implementation partner for a written scope that lists each hook, custom type, lineage path and access policy to be migrated, plus what is one-time and what is an ongoing retainer.

How long does an Atlas to OpenMetadata migration take?

No duration is given here because the answer depends on your environment, and the supplied facts do not provide a migration timeline. The main variables are the number of Atlas types and entities, the number of hooks, the depth of lineage you need to keep, and how many Ranger-controlled access paths must be re-expressed. Your team's familiarity with OpenMetadata's connectors and its 2.0 breaking changes also matters. Ask for a timeline tied to a written scope, with milestones for connector build-out, parallel running, user cutover and decommissioning Atlas.

What does the migration need from our internal team?

Expect to need staff who know Hadoop operations, Java, Airflow, identity systems and your data domains. Your Hadoop and platform engineers understand how Atlas hooks are wired into Hive, HBase and Kafka today. OpenMetadata runs on Java 21 with MySQL 8.0.42 or later or PostgreSQL 15 or later, Elasticsearch 9.x or OpenSearch 3.x, and Airflow for metadata ingestion, so you need people who can run those services. Identity integration and data owner time are often underestimated: business owners must confirm glossary terms, classifications and domain ownership. Confirm these roles and hours with your team before committing to a schedule.

Can our team do the migration itself?

Yes, if you have engineers with the skills listed above and time to run a parallel system. Many teams handle the platform build themselves and bring in outside help for the hardest parts, such as re-creating custom hooks, lineage across Kafka and Hive, or access controls. DeployOpen plans, implements, integrates and documents self-hosted open-source platforms inside your own infrastructure, usually under a fixed-scope agreement with deliverables and timelines set in writing before work starts. Weigh the skills you already have against the risk of a cutover that goes wrong, and compare that with a written quote.

Can Apache Atlas keep running during the transition?

Yes, a parallel run is the usual approach, and Atlas can keep serving its REST API and Kafka-based hook traffic while you build OpenMetadata. The practical question is whether both catalogs can receive the same metadata changes without drift. Atlas's Kafka messaging interface is the point where hooks publish, so confirm how each hook will be dual-fed or retired. Plan for the infrastructure of both systems running at once, including HBase, Solr and JanusGraph for Atlas and the OpenMetadata database and search services. Ask your team to define a cutover checklist for users, ingestion jobs and search before you turn Atlas off.

Can OpenMetadata replace Apache Ranger enforcement?

Not as a direct swap, so confirm this before you plan. Apache Atlas's README says metadata veracity is maintained with Apache Ranger to prevent unauthorized access to data at runtime, using role-based and attribute-based security. Ranger enforces access on the data systems themselves. OpenMetadata provides governance features such as glossaries, classifications, domains and its own security configuration, but those do not automatically enforce access in Hive, HBase or Kafka. Review OpenMetadata's security documentation, then map each Ranger policy to a control you can prove. Keep Ranger or an equivalent enforcement layer until you have confirmed that the replacement covers each policy.

How DeployOpen helps choose, implement or migrate

DeployOpen plans, implements, integrates, hardens and documents self-hosted open-source platforms inside the client's own infrastructure. For an Atlas-to-OpenMetadata move, we start by mapping the Atlas estate you actually run, then agree a fixed scope in writing before any migration work begins.

How we scope

We inventory the Atlas types, hooks (Hive, Kafka, HBase), Ranger policies and downstream consumers in use today.
The written scope names which lineage, glossary and classification features move, and which are retired.
We agree target infrastructure in your cloud, data center or air-gapped network, including database and search sizing.

How we build

We deploy OpenMetadata with its required Java, database, search and ingestion orchestrator dependencies in your environment.
We rebuild ingestion for your warehouses, BI tools and pipelines, then validate lineage against known Atlas results.
We configure access controls and document how they replace Ranger-based policy enforcement, with your security team reviewing it.

How we hand over

We deliver runbooks, configuration records and a cutover plan covering ingestion jobs, search and user access.
We document the Atlas decommissioning steps, including retained metadata exports and archive decisions agreed with you.
We walk your platform and data teams through daily operation, upgrades and the 2.x breaking-change checklist.

How we support

Ongoing support, upgrades and continuous testing are available as a retainer after handover.
Retainers can cover tracking maintenance releases so your team plans upgrades against a known schedule.
Any compliance evidence we prepare is for your independent auditors to assess; we do not issue audit reports.

OpenMetadata vs Apache Atlas