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DBMS > Kinetica vs. Spark SQL vs. Transbase vs. YTsaurus

System Properties Comparison Kinetica vs. Spark SQL vs. Transbase vs. YTsaurus

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparisonTransbase  Xexclude from comparisonYTsaurus  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsSpark SQL is a component on top of 'Spark Core' for structured data processingA resource-optimized, high-performance, universally applicable RDBMSYTsaurus is an open source platform for distributed storage and processing.
Primary database modelRelational DBMSRelational DBMSRelational DBMSDocument store
Key-value store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.17
Rank#334  Overall
#148  Relational DBMS
Score0.21
Rank#324  Overall
#45  Document stores
#48  Key-value stores
Websitewww.kinetica.comspark.apache.org/­sqlwww.transaction.de/­en/­products/­transbase.htmlytsaurus.tech
Technical documentationdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.transaction.de/­en/­products/­transbase/­features.htmlytsaurus.tech/­docs/­en
DeveloperKineticaApache Software FoundationTransaction Software GmbHYandex
Initial release2012201419872023
Current release7.1, August 20213.5.0 ( 2.13), September 2023Transbase 8.3, 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercial infofree development licenseOpen Source infoApache License, Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++ScalaC and C++C++
Server operating systemsLinuxLinux
OS X
Windows
FreeBSD
Linux
macOS
Solaris
Windows
Ubuntu
Data schemeyesyesyes
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
Secondary indexesyesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like DML and DDL statementsyesYQL, an SQL-based language, is supported
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
ADO.NET
JDBC
ODBC
Proprietary native API
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python
C++
Go
Java
JavaScript
Python
Server-side scripts infoStored proceduresuser defined functionsnoyes
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnoneSource-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoyesACID
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 RAMnono
User concepts infoAccess controlAccess rights for users and roles on table levelnofine grained access rights according to SQL-standardAccess Control Lists

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More resources
KineticaSpark SQLTransbaseYTsaurus
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