An intelligent semantic layer built on SQL knowledge graph technology offers a host of advanced capabilities that go beyond those provided by traditional semantic layers. Unlike conventional approaches which primarily focus on providing common, business-friendly meaning to linked datasets, an intelligent semantic layer provides advanced data management capabilities that seamlessly integrate in data-products workflows.
Other distinctive features:
Explicit Data Relationships: SQL knowledge graph technology enables the creation of complex data relationships that replace JOIN and UNION commands. This allows for up to 90% shorter queries and accelerated delivery of data products.
Ontology-Driven Data Management: An intelligent semantic layer incorporates ontologies, which define the relationships and SQL based logic rules within the data. This ensures consistency, accuracy, ease of use and a shared understanding of data across the organization, facilitating more effective data governance.
Automatic Data Enrichment: With built-in graph algorithms, an SQL knowledge graph-based semantic layer can automatically enrich data by inferring new relationships and generating additional insights. This capability streamlines data preparation and enhances the value derived from existing data.
Data Virtualization Across Sources: Joins multiple data sources at scale, so users can conveniently consume distributed/siloed data represented as a single semantic graph mapped to the semantic layer, which can be queried with short and simple SQL queries.
Data Source Integration Capabilities: Connect any data source, including Azure, Microsoft Fabric, OneLake, AWS Cloud, Google Cloud, Databricks, and Snowflake, MongoDB, databases and any file format.
Integration with Business Intelligence and Data Science Tools: ODBC and JDBC connection to any tool. In addition, seamless connection to Jupyter and Zeppelin notebooks and native access to Python’s JPype, JayDeBeAPI, and SQL-Alchemy libraries, in Apache Spark, SQL, R, Python, Scala and Java.
