Amazon DataZone vs Egeria

Amazon DataZone is a cloud service for cataloging, discovering, sharing, and governing data across AWS, on-premises, and third-party sources. Its business data catalog lets users search published data and request access. Projects give teams a way to collaborate around data assets and manage and monitor them, while governed workflows align access with users and purposes. Large language models can generate business names for structured data, including columns, to support discovery. Built-in producer sources include AWS Glue Data Catalog and Amazon Redshift; analytics integrations include Amazon Athena and Redshift Query Editor. DataZone can fulfill and manage permissions automatically for AWS Lake Formation-managed Glue tables and Redshift tables and views. Amazon SageMaker integration supports access to data and ML assets and publishing new assets to the catalog. It supports automated discovery, column-level lineage, impact analysis, and pipeline lineage. Data is encrypted at rest by default with an AWS-owned KMS key, with customer-managed keys supported for catalog data. Traffic uses TLS and client-side encryption, and service communication is over HTTPS. CloudTrail records actions. Associated AWS Glue, KMS, Athena, Redshift, and S3 usage may also incur charges.

Egeria is an open-source project for enabling metadata and context interoperability across people, tools and platforms used in AI and data science. Its open metadata schema contains more than 600 types for describing an enterprise’s digital business. Specialist servers capture, analyze, search and manage metadata, and can extract, store and synchronize information across technologies. Egeria supports metadata exchange, linking and federation across different repositories, with frameworks intended to accommodate custom types, variations and systems. Its feature catalog includes cataloguing, lineage management, discovery, metadata security and synchronized access control. Discovery functions include schema extraction, resource profiling, and data class, grain, semantic and classification discovery. Connectors cover integration, repositories, runtime, security and REST client access. Documented content packs include PostgreSQL, Microsoft SQL Server, Oracle Database, Db2 for LUW, DuckDB, Unity Catalog, Apache Atlas and Apache Kafka. Interfaces include Egeria Explorer, Lineage Explorer, Data Mesh, The Catalog, Egeria Operations and Egeria Audit. It can run in a Docker container, standalone or as part of Kubernetes deployments. The community edition is free to use.