Dataworkers vs DataHub: MCP-Native Agents vs Metadata Graph

Dataworkers vs DataHub summary: DataHub is an open-source metadata platform originally built at LinkedIn, now maintained by Acryl Data, focused on search, discovery, and lineage at scale. Dataworkers is an open-source MCP-native AI agent platform with 14 agents including catalog federation.

Both are Apache 2.0. DataHub excels at large-scale metadata graph workloads with a GraphQL backend, while Dataworkers excels at AI-agent automation across the full data engineering lifecycle — and federates across DataHub itself rather than competing for the same backend role.

DataHub is one of the most technically sophisticated open-source catalogs, with a GraphQL-based metadata graph, push + pull ingestion, and support for a huge variety of entity types. According to the DataHub documentation and Acryl's public materials, DataHub powers metadata discovery at LinkedIn, Netflix, and many other large tech companies. Dataworkers takes a different approach: instead of building another catalog backend, we federate across existing catalogs (DataHub included) and focus on agent-driven automation.

Feature Matrix

Scale and Architecture

DataHub's architecture is built for massive scale — LinkedIn operates it across millions of entities. If your organization needs a catalog that handles hundreds of thousands of tables, DataHub's metadata graph is the proven choice. Dataworkers is designed for modern cloud warehouse environments (typically thousands to tens of thousands of tables) and for AI-agent-driven workflows. We do not claim to replace DataHub at LinkedIn-scale metadata ingestion; we complement it.

Where DataHub Wins

DataHub wins when scale is your primary constraint. If you have tens of millions of metadata entities, a rich internal data producer ecosystem, and Kubernetes expertise to operate the DataHub stack, DataHub is battle-tested. Their search, discovery, and lineage at scale are industry-leading.

Where Dataworkers Wins

Dataworkers wins on agent-driven automation and MCP-native workflows. DataHub is excellent metadata infrastructure; Dataworkers is agents that act on metadata. If your team uses Claude Code or Cursor and wants AI agents that can migrate pipelines, detect drift, propose fixes, and execute them, Dataworkers is unique. Time-to-value is also faster — you can install Dataworkers in minutes versus days for DataHub.

Pattern: Run Both

The typical pattern for teams running both is to use DataHub as the metadata graph of record and Dataworkers as the AI agent layer on top. Dataworkers' catalog agent federates DataHub through our connector, so agents in Claude Code can query DataHub metadata. Explore the product or book a demo for a walkthrough.

Metadata Graph vs Agent Platform

DataHub is best described as a metadata graph platform — its core innovation is a GraphQL-based metadata model with a rich entity taxonomy, supporting datasets, dashboards, ML models, terms, users, and hundreds of other entity types. The platform is built for metadata-heavy workloads where the goal is to model every artifact in the data ecosystem and make it queryable through a unified graph API. Dataworkers is best described as an AI agent platform — the core innovation is 14 autonomous agents that execute work across the data stack through MCP tools. These are fundamentally different product categories. DataHub competes with OpenMetadata and Amundsen in the metadata graph space; Dataworkers competes with... almost nothing, because there is no other MCP-native AI agent platform for data engineering.

Ingestion Models

DataHub uses a push + pull ingestion model. The pull model uses DataHub's Python ingestion framework to extract metadata from 50+ source systems (Snowflake, BigQuery, dbt, Airflow, Tableau, Looker, etc). The push model uses DataHub emitters embedded in data pipelines to emit metadata in real time. Both are well-documented and production-hardened. Dataworkers uses a different approach: the catalog agent federates existing catalogs (DataHub included) rather than ingesting metadata into its own storage. This is lighter-weight but means Dataworkers does not store metadata independently — it queries underlying systems on demand.

Community and Commercial Support

DataHub has a large, active open-source community backed by Acryl Data. Slack channels are busy, PRs are merged regularly, and commercial support via Acryl Cloud provides an enterprise path. Dataworkers' community is newer but growing quickly. Our Discord server is live and active; contributions are welcome under Apache 2.0. Commercial support is available through Dataworkers Pro and Enterprise tiers. For teams that value a mature OSS community, DataHub is further along; for teams that want an MCP-native agent platform, Dataworkers is the only option.

Architecture Complexity

DataHub's architecture has multiple components — metadata service, metadata store (MySQL or Postgres), GraphQL service, search backend (Elasticsearch), ingestion framework, and frontend. Operating DataHub at production scale requires Kubernetes expertise and ongoing tuning. Dataworkers is simpler — the core is a set of MCP servers that run as Node.js or Python processes, with optional backends for specific use cases (audit log storage, metadata caching). This means Dataworkers is lighter to operate but less feature-rich as a standalone catalog. For teams that want a full metadata platform they can run on their own infrastructure, DataHub is a better fit; for teams that want AI agents with minimal operational overhead, Dataworkers is a better fit.

When to Pick Each

Pick DataHub if you need a full metadata catalog at massive scale and have the DevOps resources to operate it. Pick Dataworkers if you need MCP-native AI agents across the full data engineering lifecycle and want minimal operational burden. Pick both if you want DataHub as the metadata store of record and Dataworkers as the agent layer on top — this combination gives you the best of both worlds and is the most common pattern we see among customers running both products. The catalog agent's federation capability makes the integration straightforward, requiring only connector configuration rather than custom integration work.

DataHub and Dataworkers are complements, not substitutes, for most use cases. Choose DataHub for massive-scale metadata; add Dataworkers for MCP-native AI agents.