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DBMS > Amazon Neptune vs. GridGain vs. HugeGraph vs. OpenEdge vs. Splice Machine

System Properties Comparison Amazon Neptune vs. GridGain vs. HugeGraph vs. OpenEdge vs. Splice Machine

Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonGridGain  Xexclude from comparisonHugeGraph  Xexclude from comparisonOpenEdge  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudGridGain is an in-memory computing platform, built on Apache IgniteA fast-speed and highly-scalable Graph DBMSApplication development environment with integrated database management systemOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelGraph DBMS
RDF store
Key-value store
Relational DBMS
Graph DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score1.55
Rank#150  Overall
#26  Key-value stores
#70  Relational DBMS
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score3.45
Rank#85  Overall
#46  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteaws.amazon.com/­neptunewww.gridgain.comgithub.com/­hugegraph
hugegraph.apache.org
www.progress.com/­openedgesplicemachine.com
Technical documentationaws.amazon.com/­neptune/­developer-resourceswww.gridgain.com/­docs/­index.htmlhugegraph.apache.org/­docsdocumentation.progress.com/­output/­ua/­OpenEdge_latestsplicemachine.com/­how-it-works
DeveloperAmazonGridGain Systems, Inc.BaiduProgress Software CorporationSplice Machine
Initial release20172007201819842014
Current releaseGridGain 8.5.10.9OpenEdge 12.2, March 20203.1, March 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2.0commercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .NetJavaJava
Server operating systemshostedLinux
OS X
Solaris
Windows
Linux
macOS
Unix
AIX
HP-UX
Linux
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeschema-freeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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.noyesnoyes
Secondary indexesnoyesyes infoalso supports composite index and range indexyesyes
SQL infoSupport of SQLnoANSI-99 for query and DML statements, subset of DDLnoyes infoclose to SQL 92yes
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Java API
RESTful HTTP API
TinkerPop Gremlin
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
Groovy
Java
Python
Progress proprietary ABL (Advanced Business Language)C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoyes (compute grid and cache interceptors can be used instead)asynchronous Gremlin script jobsyesyes infoJava
Triggersnoyes (cache interceptors and events)noyesyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingyes infodepending on used storage backend, e.g. Cassandra and HBasehorizontal partitioning infosince Version 11.4Shared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.yes (replicated cache)yes infodepending on used storage backend, e.g. Cassandra and HBaseSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)via hugegraph-sparknoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoRelationships in graphsnoyes infoedges in graphyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnoyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Security Hooks for custom implementationsUsers, roles and permissionsUsers and groupsAccess rights for users, groups and roles according to SQL-standard

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More resources
Amazon NeptuneGridGainHugeGraphOpenEdgeSplice Machine
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