Best Data Catalog Software (2026): Top 9 Compared

Atlan is a metadata platform from Atlan Pte Ltd that presents itself as a context layer between enterprise data systems and the people and agents that query them. The core of the product is an enterprise data graph built by continuously reading warehouses, transactional databases, pipelines, BI tools and business applications, then recording the assets, lineage relationships, metric definitions, ontologies and policies that connect them. On top of that graph sit a catalog interface for search and browsing, column-level lineage views, glossary and classification management, and governance workflows covering certification, deprecation and conflict resolution.

The platform is delivered as a managed cloud service. Native connectors cover Snowflake, Databricks, BigQuery, Redshift and PostgreSQL on the data side, Tableau, Looker and Power BI for reporting, dbt and Airflow for transformation and orchestration, and Salesforce, SAP, Confluence, SharePoint and Google Drive for business context, with more than eighty integrations listed in total. A stated design goal is coexistence rather than replacement: Atlan reads from Microsoft Purview, Databricks Unity Catalog and Snowflake Horizon instead of requiring an organisation to abandon them. Named modules include Context Agents for automatic description and metric generation, a Context Engineering Studio for versioning reusable agent instructions, and an MCP server that makes catalog context available to language-model clients.

Pricing is not published. The pricing page is a contact form that routes to a sales conversation, so the entry cost, the unit of licensing and the boundary between editions are all established in negotiation. Buyers should expect an annual enterprise contract and should ask explicitly which connectors, which lineage depth and which governance workflows are included at the tier being quoted.

The fit is strongest for organisations with data spread across several clouds and BI tools that want one searchable surface without consolidating platforms first, and for teams that intend to expose governed metadata to internal agents. It is a weak fit for a small team on a single warehouse, where a cheaper catalog covers the same ground, and for buyers who require transparent list pricing before entering a procurement cycle.

Collibra Platform is the data governance and catalog suite from Collibra NV. Its distinguishing property is scope: the catalog is one module among several that share a common semantic graph and operating model. The published product stack covers Data Catalog for discovery, Data Governance for policy and stewardship, Data Privacy for regulatory workflows, Data Quality and Observability for pipeline monitoring, Data Lineage for relationship mapping, Data Marketplace for publishing curated data products, and an AI Command Center that applies the same controls to models and agents.

The semantic graph links physical assets to business terms, policies and owners, and a workflow designer automates the approval, certification and issue-resolution steps that a stewardship programme depends on. Usage and adoption monitoring reports which assets are being consumed and which documentation is stale. Integration is handled through more than one hundred native connectors, supplemented by partner-built connections and a documented API, and recent releases add natural-language querying of lineage through the Model Context Protocol.

Collibra does not publish a price list; the site routes pricing enquiries to sales, and licensing in practice is negotiated by module and by user role, with different rights for stewards, consumers and administrators. Because the platform is modular, the quoted figure depends heavily on which of the seven products are in scope, and the catalog alone prices differently from a bundle that adds quality and privacy.

The platform suits large regulated organisations, particularly in financial services, insurance, healthcare and the public sector, that need auditable evidence of who approved a definition, when a policy changed and where sensitive data flows. Those organisations usually have named data stewards and a governance council to run the workflows. It fits poorly where no such roles exist: without stewards to operate them, the workflow and policy modules that justify the price remain unused, and a simpler catalog delivers the discovery benefit at a fraction of the effort.

Alation produces one of the longest-established enterprise data catalogs. The product indexes technical metadata from databases, BI systems, files, applications and model registries, then enriches it with descriptions, definitions, policies, documentation and source links so that a table entry explains itself. Search interprets natural-language phrasing rather than requiring exact object names, and results carry lineage, tags, quality metrics, trust flags and user endorsements so that a consumer can judge whether an asset is safe to use.

Two features distinguish the product from generic metadata stores. The first is behavioural: Alation reads query logs to learn which tables and joins are actually used and by whom, and ranks and recommends accordingly. The second is Compose, a SQL authoring environment inside the catalog that lets analysts write, save and share queries against catalogued sources, which in turn produces more of the usage signal the ranking depends on. ALLIE AI drafts metadata descriptions to reduce the manual curation burden, and workflow automation adds new assets and flags compliance gaps as sources change.

Connectivity covers more than one hundred and twenty prebuilt connectors plus an Open Connector Framework for sources that are not supported out of the box, and Alation Anywhere surfaces catalog entries inside Excel, Slack and Microsoft Teams. The company lists HIPAA, ISO 27001, ISO 27701 and AICPA SOC 2 among its certifications.

Pricing is not published. The pricing page is a request form for a scoping call, so edition names, seat definitions and entry costs are settled during procurement.

Alation fits organisations with a large analyst population, a mature warehouse and enough query history for the behavioural ranking to be meaningful. It fits less well for engineering-led teams whose work happens mostly in pipelines rather than ad hoc SQL, and for small deployments where the usage signal is too thin to differentiate the product from cheaper alternatives.

Microsoft Purview is the data governance service in Microsoft's cloud, combining a Data Map that stores technical metadata with a Unified Catalog that carries business concepts such as data products, glossary terms, critical data elements and data quality rules. Scanners populate the Data Map from Azure storage and databases, Fabric, Power BI, on-premises SQL Server and a set of third-party sources, and classification rules tag sensitive fields as they are discovered. The same platform also houses Microsoft's compliance and data security tooling, so retention, eDiscovery and information protection share an administrative surface with the catalog.

Deployment is through an Azure subscription and resource group in the same tenant as the Purview account, and administration runs in the Purview portal. Integration is strongest inside the Microsoft estate: Azure Data Factory and Synapse pipelines report lineage automatically when connected, and Fabric and Power BI assets appear without additional configuration.

Billing moved to pay-as-you-go in January 2025. Unified Catalog runs two meters. The first counts unique governed assets per day, where a governed asset is a table or view linked to a governance concept; assets merely scanned into the Data Map are not billed. The published rate for the Data Catalog standard asset meter is $0.0165 per asset per day. The second meter charges data governance processing units for quality and health jobs, listed at $15 per basic unit, $60 per standard unit and $240 per advanced unit, where one unit represents sixty minutes of compute. Customers still on classic billing pay per Data Map capacity unit hour, listed at $0.411, plus scanning vCore hours at $0.63.

The service fits organisations already standardised on Azure, Fabric and Microsoft 365 that want governance inside the same tenant and billing relationship. It fits poorly for estates centred on other clouds, where connector coverage is thinner and the integration argument that justifies the choice disappears.

OpenMetadata is an open-source metadata platform whose core is released under the Apache 2.0 licence. It stores technical metadata, business semantics and organisational history in a single knowledge graph covering database schemas, dashboards, pipelines and machine learning models, and layers discovery, glossary management, classification, column-level lineage, data quality tests and collaboration features over that graph. Changes and decisions are recorded as an auditable history rather than overwritten, which matters for teams that need to explain how a definition evolved.

Deployment is self-managed. Teams run the application, its search index and its metadata store on their own infrastructure, typically in containers on Kubernetes, and schedule ingestion workflows that pull metadata from connected sources. Because the connectors and the metadata model are open, an unusual internal system can be added by writing an ingestion module rather than waiting for a vendor roadmap. The project reports several hundred code contributors and a community in the thousands, which is a reasonable proxy for connector maintenance.

The commercial path is Collate, a managed service built on the same codebase by the project's sponsoring company. Its plan page names three tiers with capacity limits rather than prices: a free tier for five users and five hundred data assets, Premium for twenty-five users and five thousand assets, and Enterprise for fifty or more users and ten thousand or more assets. Actual figures require a sales conversation, so the honest comparison is between free self-hosting and an unpublished managed price.

The platform suits engineering-led data teams with platform capacity, a preference for avoiding per-seat licensing, and a willingness to own upgrades and backups. It is a poor fit for organisations with no infrastructure staff, or for governance programmes that need vendor-supplied policy templates, formal support commitments and contractual accountability from day one.

Secoda is a data management platform aimed at teams that want catalog, documentation, lineage and basic observability without assembling several tools. The product covers search across connected sources, a catalog with table and column lineage, automated documentation, monitors and quality scoring, policy definition with role-based access control, and access request management. Bulk operations and automation rules handle repetitive governance work such as propagating ownership or tagging personally identifiable information across many assets at once. A set of specialised agents covers cataloguing, documentation, search, observability, governance and analysis tasks.

Integrations cover the common modern stack, including Snowflake, BigQuery, Databricks, dbt, Tableau, Looker, Power BI and Slack, with more than forty connectors in total. A browser extension surfaces catalog entries alongside BI dashboards. The service is offered as a managed cloud deployment, with single-tenant and self-hosted options reserved for higher tiers, and the company reports SOC 2 compliance alongside SAML single sign-on, multi-factor authentication, SSH tunnelling and encryption.

The pricing page names three tiers without figures. Core carries the catalog, table and column lineage, automations, API access, role-based access control, SAML and SSH tunnels. Premium adds the data quality score, guest accounts, policies, single-tenant deployment, PII scanning and VPC peering. Enterprise adds custom roles, self-hosted deployment, access request management, SIEM logging, unlimited integrations, disaster recovery and a dedicated account manager. Every tier routes to a sales conversation, so the practical entry price depends on which of those controls a security review demands.

Secoda suits mid-sized data teams that need documentation and lineage working within weeks and value breadth over depth in any single area. It fits poorly for enterprises with formal stewardship councils and multi-stage approval requirements, where the configurable workflow engines of the larger suites do work that Secoda's automation rules do not attempt.

Dataplex Universal Catalog is Google Cloud's metadata and governance service, and the successor to the older Data Catalog product, which the pricing documentation describes as being in a deprecation phase with a migration path to the newer service. It maintains an inventory of data assets across BigQuery, Cloud Storage and other Google Cloud services, supports search across that inventory, records lineage between entities, and runs data profiling and data quality scans against registered tables.

Custom metadata is modelled through aspects and aspect types: an organisation defines the shape of the metadata it wants to attach, such as ETL provenance, governance ownership or quality attributes, and applies those aspects to entries. The documentation works through sizing in those terms, noting that ten dollars of monthly metadata storage holds roughly five gigabytes, which corresponds to about five million small aspects of a kilobyte each or five hundred thousand aspects of ten kilobytes.

Billing is pay-as-you-go across two published SKU families. Metadata storage is charged at $2.00 per gibibyte per month, measured as a monthly average of stored metadata, and technical metadata ingested automatically from Google Cloud services such as BigQuery carries no storage charge. Processing is charged per data compute unit, listed at $0.06 per DCU for standard processing and $0.089 for premium processing in the reference regions, with rates varying by location. A free monthly allowance applies to standard processing only, not to the premium tier.

The service fits organisations whose analytical estate is centred on BigQuery and Cloud Storage and who want governance billed alongside the rest of their Google Cloud usage. It is a weaker choice for heterogeneous estates: sources outside Google Cloud require custom ingestion, and the business-facing curation features are less developed than those of the dedicated catalog vendors.

DataHub is an open-source metadata platform maintained by Acryl Data with a large external contributor base. It provides search and discovery over catalogued assets, end-to-end lineage with impact analysis so that the downstream effect of a schema change can be traced before it is made, and governance features covering ownership, access policies and data quality signals. The project reports adoption across thousands of companies and several million monthly package downloads, which is a useful indicator of connector maintenance and community support.

The architecture is event-driven: metadata change events flow through a stream into a graph and search layer, which is why the metadata model can be extended with custom entity and aspect types rather than being fixed by the vendor. That extensibility is the main technical argument for choosing DataHub over a closed catalog, and it is also the main operational cost, since a self-hosted deployment involves running the stream, the search index and the metadata store as well as the application itself. Ingestion runs as scheduled recipes against connected sources.

Two editions exist. DataHub Core is the self-hosted open-source distribution and carries no licence fee. DataHub Cloud is the managed commercial service, adding hosted infrastructure, additional features and vendor support; its price is not published and requires a sales conversation. Recent releases add a context management layer intended to serve metadata to AI agents and integrations with common agent frameworks.

DataHub suits platform engineering teams that already operate streaming infrastructure, want to model internal systems that no commercial catalog covers, and can absorb the maintenance. It fits poorly for business-led governance programmes: the curation, stewardship workflow and policy tooling that regulated buyers expect is thinner than in the commercial suites, and the interface assumes a technically confident audience.

The AWS Glue Data Catalog is the metadata repository underneath the AWS analytics stack rather than a business-facing catalog in the sense of the other entries here. It stores databases, tables, table versions, partitions, partition indexes and column statistics, and acts as the shared metastore that Amazon Athena, Amazon Redshift Spectrum, Amazon EMR and Glue's own Spark jobs read from, so a table defined once is queryable from several engines without redefinition.

Crawlers connect to a source, apply a prioritised list of classifiers to infer schema, and write the result into the catalog on a schedule, on demand or in response to a trigger, which keeps partition and schema information current as files land in Amazon S3. The catalog computes table and column statistics that Athena and Redshift use for query planning, and a schema registry validates the evolution of streaming payloads against registered Avro schemas for Kafka, Amazon MSK, Kinesis, Flink and Lambda consumers. From Glue 5.0 onwards, table, column and row level permissions apply to Spark jobs reading Apache Iceberg, Apache Hudi and Delta tables, with AWS Lake Formation providing the permission model.

Pricing is consumption-based and unusually transparent. The first million metadata objects stored are free and the first million accesses are free; beyond that, storage costs $1.00 per 100,000 objects per month. A metadata object is a table, table version, partition, partition index, statistic, database or catalog, so partition-heavy lakes accumulate objects faster than table counts suggest.

The service fits AWS-centred engineering teams that need a reliable schema store at negligible cost, and it is often deployed underneath one of the commercial catalogs rather than instead of one. It does not fit organisations looking for glossaries, stewardship workflows, endorsements or a search experience aimed at business analysts, none of which it attempts to provide.