Collate vs. DataHub

Collate offers AskCollate for conversational AI, AI Studio with 7 specialized agents (Ingestion, Lineage, Documentation, Classification, Tiering, Quality, SQL) that automate governance tasks out of the box, along with the ability to build custom agents via UI. An enterprise-grade MCP Server is available native within Collate with full permissioning controls and security. DataHub has no equivalent to Collate's pre-built agents or custom UI-built agents, and teams must build their own AI workflows on top of DataHub's Python framework. DataHub's MCP service is an add-on service without permissions controls. Additionally, Collate's AI services act on a Semantic Context Graph with portable RDF/DCAT semantics for understanding of the data landscape, while DataHub's run on a Kafka-based metadata service with more limited PDL schemas.

Collate has offered native, comprehensive data quality since day one with 25+ built-in test types, DQ as Code, anomaly detection, incident management, and DataDiff across 30+ database connectors, all included at no extra cost. DataHub offers configurable assertions with anomaly detection (Smart Assertions), but with fewer discrete test types, more limited connector coverage for quality scanning, and basic incident tracking with enhanced incident management still on the roadmap.

DataHub inherited its Kafka-based architecture from LinkedIn, where Kafka was originally created. Kafka was designed for large-scale event pipelines, not metadata ingestion. No major data warehouse (Snowflake, Databricks, BigQuery) emits real-time metadata events, so the Kafka layer adds operational cost and complexity without delivering true real-time benefits. Collate uses an API-first architecture with 4 core components instead of DataHub's 11, resulting in faster deployment, simpler operations, and benchmarked 18x faster metadata ingestion from Redshift. Collate's CTO, Harsha Chintalapani, is a Kafka PMC contributor who scaled Kafka to 5 trillion events per day at Uber and deliberately chose not to use Kafka for metadata ingestion.

Yes, both are Apache 2.0 licensed with full source code availability. The key difference is metadata standards, not licensing. Collate uses JSON Schema, a universal standard supported by every programming language and natively understood by LLMs. DataHub uses PDL (Pegasus Definition Language), developed for LinkedIn's internal use with limited adoption outside LinkedIn. As DataHub's own co-creator Mars Lan acknowledged, 'DataHub is using Pegasus, which is a very weird language developed by LinkedIn. Even though it's open source, it's not super popular outside LinkedIn.'

Collate uses incremental extraction by default, syncing only metadata that changed since the last successful pipeline run. This approach has been benchmarked at up to 18x faster than DataHub for Redshift ingestion (10 minutes vs. 3 hours). DataHub performs full metadata scans on every scheduled run through its Kafka-based pipeline. For large data estates, the difference in ingestion time, compute costs, and source system load is substantial.

Collate provides UI-driven data contract creation where producers and consumers jointly author contracts with schema validation, SLA tracking, freshness guarantees, and built-in quality tests tied directly to the contract. Contracts include run history with pass/fail status over time and support YAML import/export for Open Data Contract Standard compatibility. DataHub supports contract creation through its UI, but contracts require assembling pre-built assertions separately, and scheduled enforcement is available in the SaaS offering only.

Yes. Both Collate (via OpenMetadata) and DataHub offer self-hosted deployment under Apache 2.0 licensing. The key architectural difference is operational complexity. Collate runs on 4 core components (Jetty, MySQL, ElasticSearch, and the application) with no Kafka dependency. DataHub requires 11 components including Kafka, Zookeeper, and multiple microservices, resulting in higher infrastructure costs and operational overhead. Both also offer managed SaaS options, and Collate additionally provides BYOC and Hybrid Runner deployment models.

Collate uses JSON Schema for strongly typed, self-documenting metadata that is natively understood by LLMs. It supports RDF/DCAT for semantic richness and the Open Data Contract Standard for portable data contracts. DataHub uses PDL (Pegasus Definition Language), a LinkedIn-developed format with limited tooling outside the Java ecosystem. While DataHub data is exportable, the PDL format may require conversion for integration with external systems and LLMs.

Mars Lan, co-creator of DataHub, made notable comments on the Data Engineering Podcast in 2023. He said, 'We kind of joke about OpenMetadata being a better version of DataHub' and acknowledged that 'DataHub is subjected to some of the technological choices that LinkedIn placed limits on.' He also noted that OpenMetadata 'didn't choose to build a point solution. They chose to build a platform' and praised the choice of JSON Schema as 'very well understood, standardized, and has a strong connection to Open APIs.'