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DBMS > Hive vs. RDFox vs. Spark SQL vs. Yanza

System Properties Comparison Hive vs. RDFox vs. Spark SQL vs. Yanza

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Editorial information provided by DB-Engines
NameHive  Xexclude from comparisonRDFox  Xexclude from comparisonSpark SQL  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
Descriptiondata warehouse software for querying and managing large distributed datasets, built on HadoopHigh performance knowledge graph and semantic reasoning engineSpark SQL is a component on top of 'Spark Core' for structured data processingTime Series DBMS for IoT Applications
Primary database modelRelational DBMSGraph DBMS
RDF store
Relational DBMSTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score0.23
Rank#308  Overall
#25  Graph DBMS
#14  RDF stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitehive.apache.orgwww.oxfordsemantic.techspark.apache.org/­sqlyanza.com
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.oxfordsemantic.techspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation infoinitially developed by FacebookOxford Semantic TechnologiesApache Software FoundationYanza
Initial release2012201720142015
Current release3.1.3, April 20226.0, Septermber 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoApache 2.0commercial infofree version available
Cloud-based only infoOnly available as a cloud servicenononono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++Scala
Server operating systemsAll OS with a Java VMLinux
macOS
Windows
Linux
OS X
Windows
Windows
Data schemeyesyes infoRDF schemasyesschema-free
Typing infopredefined data types such as float or dateyesyesyesno
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.nono
Secondary indexesyesnono
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
Thrift
RESTful HTTP API
SPARQL 1.1
JDBC
ODBC
HTTP API
Supported programming languagesC++
Java
PHP
Python
C
Java
Java
Python
R
Scala
any language that supports HTTP calls
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenono
Triggersnonoyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorreplication via a shared file systemnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency in stand-alone mode, Eventual Consistency in replicated setupsImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnono
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.yesno
User concepts infoAccess controlAccess rights for users, groups and rolesRoles, resources, and access typesnono

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More resources
HiveRDFoxSpark SQLYanza
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