Neo4J stores highly connected data in a graph format rather than in tables (tables are more suitable for aggregated data). In fact, data is stored in nodes connected to each other by defined relationship. Both nodes and relationships have their properties. It is an embedded, disk-based, fully transactional Java persistence engine with few small jars. It offers high scalability allowing to add up to several billion of data, high availability even if data is distributed across many machines, fast queries and rapid path identification through its traversal framework. In addition, data analysts can use its human readable query language adapted for graph models. Neo4j offers also a convenient and simple access (via rest interface or an object-oriented Java API). Similarly to relational databases, it ensures full ACID properties for reliable transactions (Kaur and Rani, 2013).
Neo4j spatial is a library of utilities for Neo4j that permits to add spatial indexes to already located data. It is designed to facilitate spatial operations on data (to search
for specific data by region or within a defined perimeter). The classes enable to use the geotools through some applications such as Geoserver and uDig.
OrientDB is an open source NoSQL database management system that is released under the Apache 2 license. It is written in Java so it can run on Linux, Windows and any system that supports Java. It provides the flexibility of documents as well as a good performance to handle distributed graphs. In fact, OrientDB is a document-based database that consists of ODocuments (possibility to dynamically add and remove properties). At the same time, it allows to manage relationships as in graph databases with direct connections between records. Therefore, it emulates the property of index free adjacency of documents. It supports multiple modes including schema- less, schema-full and schema-mixed. Furthermore, it supports transactions and ACID properties. It can store up to 150,000 records per second.
As mentioned before, OrientDB supports SQL as its query language. On the contrary, ArangoDB provides its own query language called AQL. This one enables aggregation, graph queries, grouping, joins, list iteration, results filtering, results projection, sorting and variables.
In fact, ArangoDB query language (AQL) is designed to support complex queries especially on ArangoDB data models. The storage and retrieval of data is based on collections. AQL is considered as a declarative language focusing on the results instead of how the results should be produced. Furthermore, AQL is independent of the programming language of the clients. Thus, all the clients use the same language and syntax. In addition, it offers REST option for querying documents and permits to query by example. It enables a vertical and horizontal sharding (i.e., to add more computation power, to shard data to many servers). It runs on different platforms such us Linux, Windows, OSX and even Raspberry Pi. It is available under Apache 2 license.
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