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DBMS > Kinetica vs. RDFox vs. Spark SQL vs. SWC-DB

System Properties Comparison Kinetica vs. RDFox vs. Spark SQL vs. SWC-DB

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonRDFox  Xexclude from comparisonSpark SQL  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsHigh performance knowledge graph and semantic reasoning engineSpark SQL is a component on top of 'Spark Core' for structured data processingA high performance, scalable Wide Column DBMS
Primary database modelRelational DBMSGraph DBMS
RDF store
Relational DBMSWide column store
Secondary database modelsSpatial DBMS
Time Series DBMS
Relational DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.23
Rank#308  Overall
#25  Graph DBMS
#14  RDF stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.01
Rank#376  Overall
#13  Wide column stores
Websitewww.kinetica.comwww.oxfordsemantic.techspark.apache.org/­sqlgithub.com/­kashirin-alex/­swc-db
www.swcdb.org
Technical documentationdocs.kinetica.comdocs.oxfordsemantic.techspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaOxford Semantic TechnologiesApache Software FoundationAlex Kashirin
Initial release2012201720142020
Current release7.1, August 20216.0, Septermber 20223.5.0 ( 2.13), September 20230.5, April 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open Source infoGPL V3
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++C++ScalaC++
Server operating systemsLinuxLinux
macOS
Windows
Linux
OS X
Windows
Linux
Data schemeyesyes infoRDF schemasyesschema-free
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.nonono
Secondary indexesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL-like DML and DDL statementsSQL-like query language
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
RESTful HTTP API
SPARQL 1.1
JDBC
ODBC
Proprietary protocol
Thrift
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
C
Java
Java
Python
R
Scala
C++
Server-side scripts infoStored proceduresuser defined functionsnono
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationreplication via a shared file systemnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency in stand-alone mode, Eventual Consistency in replicated setupsImmediate Consistency
Foreign keys infoReferential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.yes infoGPU vRAM or System RAMyesnono
User concepts infoAccess controlAccess rights for users and roles on table levelRoles, resources, and access typesno

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More resources
KineticaRDFoxSpark SQLSWC-DB infoSuper Wide Column Database
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