Chimera: A Bridge Between Big Data Analytics and Semantic Technologies

In the last decades, Knowledge Graph (KG) empowered analytics have been used to extract advanced insights from data. Several companies integrated legacy relational databases with semantic technologies using Ontology-Based Data Access (OBDA). In practice, this approach enables the analysts to write SPARQL queries both over KGs and SQL relational data sources by making transparent most of the implementation details. However, the volume of data is continuously increasing, and a growing number of companies are adopting distributed storage platforms and distributed computing engines. There is a gap between big data and semantic technologies. Ontop, one of the reference OBDA systems, is limited to legacy relational databases, and the compatibility with the big data analytics engine Apache Spark is still missing. This paper introduces Chimera, an open-source software suite that aims at filling such a gap. Chimera enables a new type of round-tripping data science pipelines. Data Scientists can query data stored in a data lake using SPARQL through Ontop and SparkSQL while saving the semantic results of such analysis back in the data lake. This new type of pipelines semantically enriches data from Spark before saving them back.