- Connects data and analytical processes, serving as an integrated architectural layer. - Supports data design, deployment, and usage across all environments and platforms. - Aims to speed up insights inference from data and manage data flow and curation.
- Enables frictionless data access and sharing in distributed data environments. - Provides unified and consistent data management framework across previously siloed storage. - Simplifies complexity without the need for additional data platforms.
- Enable self-service data consumption and collaboration. - Automate governance, data protection, and security enabled by active metadata. - Automate data engineering tasks and augment data integration for improved efficiency.
- Well-connected pool of metadata. - Activation of metadata through knowledge graphs and analytics. - Data catalog providing access to all metadata types. - Set of standard data integration tools. - Strong data compatibility backbone for various data types.
- Reduces workloads for both humans and machines by automating repetitive tasks. - Helps diagnose and correct data integration issues. - Provides insights for better understanding business needs and gaining a competitive advantage. - Ensures consistency across integrated environments.
