Amazon Neptune vs. Apache TinkerPop vs. HyperGraphDB vs. python-sql Comparison Chart

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security. Proactively detect and investigate IT infrastructure using a layered security approach. Visualize all infrastructure to plan, predict and mitigate risk. Build graph queries for near-real-time identity fraud pattern detection in financial and purchase transactions.

HyperGraphDB is a general purpose, open-source data storage mechanism based on a powerful knowledge management formalism known as directed hypergraphs. While a persistent memory model designed mostly for knowledge management, AI and semantic web projects, it can also be used as an embedded object-oriented database for Java projects of all sizes. Or a graph database, or a (non-SQL) relational database. HyperGraphDB is a storage framework based on generalized hypergraphs as its underlying data model. The unit of storage is a tuple made up of 0 or more other tuples. Each such tuple is called an atom. One could think of the data model as relational where higher-order, n-ary relationships are allowed or as graph-oriented where edges can point to an arbitrary set of nodes and other edges. Each atom has an arbitrary, strongly-typed value associated with it. The type system managing those values is embedded as a hypergraph and customizable from the ground up.