Once you have the basic understanding of how to structure the data associated with graph analysis and what use cases it can leverage, you must understand the terminology of graph analysis. This brief glossary includes several essential graph analytics terms. Neo4J is the most widely used graph database. It is open source and optimized to scale both vertically and horizontally to handle vast graph networks. The Cypher language is used for querying and updating a graph store in Neo4J. The Resource Description Framework (RDF) is a standard specification for modeling graph data. Some graph databases are based on this specification; AllegroGraph and MarkLogic are examples of RDF data stores. SPARQL is a protocol associated with querying the RDF structure. Gremlin, a graph traversal language developed by Apache, works for both OLTP-based graph databases and OLAP-based graph processors. Python is the most popular language associated with data analysis. It supports built-in data structures as well as dynamic typing and binding. R is the second most popular; this language and environment are optimized for statistical computing and visualization. SQL refers to the American National Standards Institute (ANSI) standard for querying relational databases. Although graph databases are optimized for graph analysis, much of the data used for graph analysis lies in a relational database. Data extraction often must be accomplished by leveraging SQL. Scala is a fully functional, object-oriented language. It is highly interoperable with Java through the Java Virtual Machine (JVM). Read more: https://tdwi.org/articles/2019/06/14/arch-all-how-to-incorporate-graph-analytics.aspx