Is SPARQL the SQL for NoSQL?

NoSQL systems have been solving the scalability challenge associated with semi-structured and unstructured data, but how will NoSQL address 30 times more unstructured data with 30 times fewer engineers than structured data and whose skills are fragmented over 150 different NoSQL systems? How will NoSQL systems interface to industry standard business intelligence tools?

NoSQL needs a query language that can operate without a schema, with varying key value types, nonhomogeneous collections of data, and substructure within the data records. The SQL for NoSQL is SPARQL. W3C SPARQL is a standard query language designed for RDF that is well suited to schema free key value stores. The semantic community views SPARQL as a way to bring Big Data to semantics, whereas SPARQL brings analytics to NoSQL systems. This talk describes SPARQL implementations on NoSQL, SPARQL extensions for analytics, and typical use cases for using SPARQL on NoSQL.

Arthur is a successful semantic graph analytics scientist applying his talents to developing customers’ product strategies and architectures for analytics and decision support. He has a deep understanding of graph analytics, predictive modeling, unstructured data, categorization, text mining, natural language processing, data mining algorithms, neural networks, and Artificial Intelligence. He has used his expertise in these areas to provide thought leadership and develop applications and evaluations in multiple domains including intelligence/security informatics, business intelligence, cyber security, financial analysis, corporate governance, retail and energy. He has served as CTO, chief scientist, and chief software architect and founded Keen Analytics, to provide semantic graph analytics services. He is actively involved in several standards organizations and has been invited to speak at the NoSQL, SemTech and SXSW conferences. He has 2 patents for applying graph analytics in Cyber.