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In the AIera, enterprises need to collect more data to build high-quality AI applications, including structured data (databases, data warehouses, etc.) and unstructured data (data lakes, document libraries, real-time data, etc.). Data integrity and compliance play a key role in building AI applications, which is the value of metadata. Providing AI users with a unified data view so that they can better discover and use multi-source heterogeneous data, including data discovery, data semantics, data lineage, data permissions, etc., and managing the data life cycle in combination with enterprise governance needs to avoid resource waste and security issues, has become a strong need for every enterprise.
Apache Gravitino provides a unified API to access multiple data sources and multiple data storages, supports multiple data engines and machine learning frameworks to access data, and implements unified naming, unified permissions, unified lineage, unified auditing and other functions based on unified metadata, thereby greatly simplifying the data operation and breaking the data silos. At present, it has been adopted by companies such as Xiaomi, Bilibili, Pinterest, and Uber, and has achieved good results. This session will introduce the background, architecture, core functions and use cases of Gravitino.
