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DBMS > 4D vs. AnzoGraph DB vs. Neo4j vs. Spark SQL

System Properties Comparison 4D vs. AnzoGraph DB vs. Neo4j vs. Spark SQL

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Editorial information provided by DB-Engines
Name4D infoformer name: 4th Dimension  Xexclude from comparisonAnzoGraph DB  Xexclude from comparisonNeo4j  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionApplication development environment with integrated database management systemScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationScalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offeringsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSGraph DBMS
RDF store
Graph DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.68
Rank#110  Overall
#54  Relational DBMS
Score0.27
Rank#302  Overall
#24  Graph DBMS
#13  RDF stores
Score44.47
Rank#23  Overall
#1  Graph DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Websitewww.4d.comcambridgesemantics.com/­anzographneo4j.comspark.apache.org/­sql
Technical documentationdeveloper.4d.comdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmneo4j.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
Developer4D, IncCambridge SemanticsNeo4j, Inc.Apache Software Foundation
Initial release1984201820072014
Current releasev20, April 20232.3, January 20215.18.1, March 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercialcommercial infofree trial version availableOpen Source infoGPL version3, commercial licenses availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Neo4j Aura: Neo4j’s fully managed cloud service: The zero-admin, always-on graph database for cloud developers.
Implementation languageJava, ScalaScala
Server operating systemsOS X
Windows
LinuxLinux infoCan also be used server-less as embedded Java database.
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeyesSchema-free and OWL/RDFS-schema supportschema-free and schema-optionalyes
Typing infopredefined data types such as float or dateyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.yesnono
Secondary indexesyesnoyes infopluggable indexing subsystem, by default Apache Luceneno
SQL infoSupport of SQLyes infoclose to SQL 92SPARQL and SPARQL* as primary query language. Cypher preview.noSQL-like DML and DDL statements
APIs and other access methodsODBC
RESTful HTTP API infoby using 4D Mobile
SOAP webservices
Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
Bolt protocol
Cypher query language
Java API
Neo4j-OGM infoObject Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3
JDBC
ODBC
Supported programming languages4D proprietary IDE
PHP
C++
Java
Python
.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesuser defined functions and aggregatesyes infoUser defined Procedures and Functionsno
Triggersyesnoyes infovia event handlerno
Partitioning methods infoMethods for storing different data on different nodesnoneAutomatic shardingyes using Neo4j Fabricyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationMulti-source replication in MPP-ClusterCausal Clustering using Raft protocol infoavailable in in Enterprise Version onlynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoKerberos/HDFS data loadingno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency in MPP-ClusterCausal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode
Foreign keys infoReferential integrityyesno infonot needed in graphsyes infoRelationships in graphsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlUsers and groupsAccess rights for users and rolesUsers, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)no
More information provided by the system vendor
4D infoformer name: 4th DimensionAnzoGraph DBNeo4jSpark SQL
Specific characteristicsNeo4j delivers graph technology that has been battle tested for performance and scale...
» more
Competitive advantagesNeo4j is the market leader, graph database category creator, and the most widely...
» more
Typical application scenariosReal-Time Recommendations Master Data Management Identity and Access Management Network...
» more
Key customersOver 800 commercial customers and over 4300 startups use Neo4j. Flagship customers...
» more
Market metricsNeo4j boasts the world's largest graph database ecosystem with more than 140 million...
» more
Licensing and pricing modelsGPL v3 license that can be used all the places where you might use MySQL. Neo4j Commercial...
» more
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