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DBMS > Kinetica vs. Spark SQL vs. SQream DB vs. Yaacomo

System Properties Comparison Kinetica vs. Spark SQL vs. SQream DB vs. Yaacomo

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparisonSQream DB  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionFully vectorized database across both GPUs and CPUsSpark SQL is a component on top of 'Spark Core' for structured data processinga GPU-based, columnar RDBMS for big data analytics workloadsOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMSRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.70
Rank#227  Overall
#104  Relational DBMS
Websitewww.kinetica.comspark.apache.org/­sqlsqream.comyaacomo.com
Technical documentationdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.sqream.com
DeveloperKineticaApache Software FoundationSQream TechnologiesQ2WEB GmbH
Initial release2012201420172009
Current release7.1, August 20213.5.0 ( 2.13), September 20232022.1.6, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialcommercial
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.
Implementation languageC, C++ScalaC++, CUDA, Haskell, Java, Scala
Server operating systemsLinuxLinux
OS X
Windows
LinuxAndroid
Linux
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyes, ANSI Standard SQL Typesyes
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 indexesyesnonoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like DML and DDL statementsyesyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
.Net
JDBC
ODBC
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functionsnouser defined functions in Python
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Corehorizontal and vertical partitioninghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnonenoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACID
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.yes infoGPU vRAM or System RAMnoyes
User concepts infoAccess controlAccess rights for users and roles on table levelnofine grained access rights according to SQL-standard

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More resources
KineticaSpark SQLSQream DBYaacomo
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