The emerging Data Lakehouse approach is bringing the best of Data Warehouses and Data Lakes in one simple platform to co-locate data from across the enterprise for cost effective analytics and AI use cases. But, despite the promise of Data Lakehouses, they still leave much of the data unconnected and in native form which can require significant effort to unlock its full potential.
Industry analysts recognize the power of Knowledge Graphs in delivering integrated, trusted, and real-time views of enterprise data. Knowledge Graphs excel at delivering a semantic layer which unifies business data with knowledge bases, industry terms, and domain knowledge.
By overlaying a Knowledge Graph onto a Lakehouse architecture the combination facilitates more flexible data operations, lowers data integration costs, and delivers powerful insights only possible when data is connected. Adding a Knowledge Graph to your Lakehouse will enable your organization to explore and exploit unknown connections across your data for richer analytics and enhanced Artificial Intelligence capabilities.
Franz’s AllegroGraph platform further extends this Knowledge Graph and Lakehouse combination with a novel Entity-Event Model. This production proven architecture puts core “entities” such as customers, patients, students, or people of interest at the center and then collects several layers of knowledge related to the entity as “events” in temporal context. Adding Franz’s Entity-Event Knowledge Graph to your Lakehouse delivers enhanced discovery, greatly reduced data complexity, and faster results—at scale.
Take your Lakehouse investment to the next level with AllegroGraph’s Entity-Event semantic layer.