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DBMS > Amazon Neptune vs. DataFS vs. Kinetica vs. Stardog

System Properties Comparison Amazon Neptune vs. DataFS vs. Kinetica vs. Stardog

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonDataFS  Xexclude from comparisonKinetica  Xexclude from comparisonStardog  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudAll data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.Fully vectorized database across both GPUs and CPUsEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelGraph DBMS
RDF store
Object oriented DBMSRelational DBMSGraph DBMS
RDF store
Secondary database modelsGraph DBMSSpatial DBMS
Time Series 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
Score0.09
Rank#360  Overall
#18  Object oriented DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score2.07
Rank#122  Overall
#11  Graph DBMS
#6  RDF stores
Websiteaws.amazon.com/­neptunenewdatabase.comwww.kinetica.comwww.stardog.com
Technical documentationaws.amazon.com/­neptune/­developer-resourcesdev.mobiland.com/­Overview.xspdocs.kinetica.comdocs.stardog.com
DeveloperAmazonMobiland AGKineticaStardog-Union
Initial release2017201820122010
Current release1.1.263, October 20227.1, August 20217.3.0, May 2020
License infoCommercial or Open Sourcecommercialcommercialcommercialcommercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++Java
Server operating systemshostedWindowsLinuxLinux
macOS
Windows
Data schemeschema-freeClasses, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)yesschema-free and OWL/RDFS-schema support
Typing infopredefined data types such as float or dateyesyesyesyes
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.nononono infoImport/export of XML data possible
Secondary indexesnonoyesyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLnonoSQL-like DML and DDL statementsYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
.NET Client API
Proprietary client DLL
WinRT client
JDBC
ODBC
RESTful HTTP API
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
.Net
C
C#
C++
VB.Net
C++
Java
JavaScript (Node.js)
Python
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresnouser defined functionsuser defined functions and aggregates, HTTP Server extensions in Java
Triggersnono, except callback-events from server when changes happenedyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesnoneProprietary Sharding systemShardingnone
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.Source-replica replicationMulti-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency in HA-Cluster
Foreign keys infoReferential integrityyes infoRelationships in graphsyesyesyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Windows-ProfileAccess rights for users and roles on table levelAccess rights for users and roles

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More resources
Amazon NeptuneDataFSKineticaStardog
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