Yugen SDL: Semantic Data Lake Design for Relational Database from Enterprise Data Platforms

Although data lake technology has received increasing research attention in recent years with the popularity of Big Data and heterogeneous data technologies, it still receives little attention in the enterprise space and relation to relational databases. Since enterprise data lakes include structured data (rows and columns) and semi-structured data like CSV from relational databases, etc. [1], relational data requires a complete real-time corresponding data management platform to handle this data. Yet research challenges for these still exist. The Yugen Semantic Data Lake (Yugen SDL) for data enterprise data involved in this research is designed to solve this problem, improving the handling of relational databases by data lakes in the enterprise domain. In this project, a traditional Big Data platform and a complete real-time relational system are built, a semantic layer is defined, a knowledge graph is created, and data is managed in a unified way for SPARQL queries. A bespoke query engine is built, and the structured data generated by the internal IT systems in the data lake is queried using the D2RQ tool. In contrast, the internal semi-structured data is managed by querying using TARQL. Yugen SDL will focus on the data needs of the enterprise and address the critical challenges of developing large-scale SDLs for relational data and big data platforms.