
AWS Glue DataBrew
7.2#2 in schema registry softwareWebAPI
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| Fact | AWS Glue DataBrew | DataHub |
|---|---|---|
| API | AWS provides an API reference for AWS Glue DataBrew operations. | DataHub documents GraphQL and REST interfaces and provides CLI commands for GraphQL operations, search, entity retrieval and metadata changes. |
| Compliance | AWS says third-party auditors assess DataBrew within compliance programs including SOC, PCI, FedRAMP and HIPAA. | Gartner's product profile states that DataHub Cloud supports SOC II and HIPAA compliance and offers in-VPC execution for sensitive environments. |
| Documentation depth | The developer guide covers concepts, setup, datasets, integrations, security, quotas, jobs, recipes, profiles and troubleshooting topics. | The documentation site includes integration guides, API and SDK references, deployment, authentication, authorization, developer and release-history sections. |
| Integrations | DataBrew datasets can use files, Amazon S3, supported JDBC data sources, or the AWS Glue Data Catalog. | The project advertises more than 150 integrations, including Snowflake, BigQuery, dbt, Databricks, Looker, Tableau, Power BI, Airflow, Kafka, S3 and Iceberg. |
| Lineage | DataBrew visually maps data lineage across data sources and transformation steps. | DataHub describes cross-platform, column-level lineage that traces data from source through transformations and AI models to downstream assets. |
| Purpose | AWS Glue DataBrew is a visual tool for preparing data for analytics and machine learning without writing code. | DataHub describes its platform as a context platform that unifies technical metadata, business knowledge, and documentation for enterprise data and AI agents. |
| Rating on G2 | 4.3 out of 5 from 196 reviews. | 4.4 out of 5 from 8 reviews |
| Security | AWS documentation recommends IAM access controls, TLS for communications, CloudTrail activity logging, and AWS encryption solutions. | DataHub Cloud states 99.5% uptime, fine-grained access controls, AWS PrivateLink, IP restrictions, in-VPC remote execution and SOC 2 compliance. |
It suits data analysts and data scientists preparing data for analytics or machine learning, especially those who want visual transformations instead of writing code. It can also serve teams working with large datasets across data lakes, warehouses, and databases.
DataHub suits enterprise data teams seeking a catalog that connects metadata, documentation, lineage, and AI tools. Core may suit teams willing to operate a self-hosted platform, while Cloud provides managed service and support.