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DBMS > Apache Phoenix vs. Kinetica vs. Spark SQL vs. SWC-DB vs. Transbase

System Properties Comparison Apache Phoenix vs. Kinetica vs. Spark SQL vs. SWC-DB vs. Transbase

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparisonTransbase  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseFully vectorized database across both GPUs and CPUsSpark SQL is a component on top of 'Spark Core' for structured data processingA high performance, scalable Wide Column DBMSA resource-optimized, high-performance, universally applicable RDBMS
Primary database modelRelational DBMSRelational DBMSRelational DBMSWide column storeRelational DBMS
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.26
Rank#94  Overall
#50  Relational DBMS
Score0.74
Rank#202  Overall
#97  Relational DBMS
Score22.94
Rank#34  Overall
#20  Relational DBMS
Score0.09
Rank#309  Overall
#13  Wide column stores
Score0.08
Rank#310  Overall
#139  Relational DBMS
Websitephoenix.apache.orgwww.kinetica.comspark.apache.org/­sqlgithub.com/­kashirin-alex/­swc-db
www.swcdb.org
www.transaction.de/­en/­solutions/­transbase-resource-optimized-high-performance-database
Technical documentationphoenix.apache.orgwww.kinetica.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.transaction.de/­en/­transbase-inside
DeveloperApache Software FoundationKineticaApache Software FoundationAlex KashirinTransaction Software GmbH
Initial release20142012201420201987
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20197.1, August 20213.2.0, October 20210.5, April 2021Transbase 8.4, September 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache 2.0Open Source infoGPL V3commercial infofree development license
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC, C++ScalaC++C and C++
Server operating systemsLinux
Unix
Windows
LinuxLinux
OS X
Windows
LinuxFreeBSD
Linux
macOS
Solaris
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyesschema-freeyes
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.nonononono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLyesSQL-like DML and DDL statementsSQL-like DML and DDL statementsSQL-like query languageyes
APIs and other access methodsJDBCJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
Thrift
ADO.NET
JDBC
ODBC
Proprietary native API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C++C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsnonoyes
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangenonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replicationnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoyes
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoGPU vRAM or System RAMnonono
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users and roles on table levelnofine grained access rights according to SQL-standard
More information provided by the system vendor
Apache PhoenixKineticaSpark SQLSWC-DB infoSuper Wide Column DatabaseTransbase
Specific characteristicsNative and fully vectorized database across both GPUs and CPUs, with memory first...
» more
Competitive advantagesBest in class geospatial and temporal analytics. Lockless architecture provides real-time...
» more
Typical application scenariosAny application requiring (1) real time insights, (2) time series analysis (3) geospatial...
» more
Key customersCitibank, US Air Force, Softbank, OVO, Telkomsel, USPS, 2 of the top 3 US Telcos,...
» more
Market metricsThe largest IoT deployment in the world, NORAD, runs on Kinetica.
» more
Licensing and pricing modelsAlways Free Tier in the Cloud, Pay-As-You-Go Consumption Based Pricing, and Portable...
» more

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More resources
Apache PhoenixKineticaSpark SQLSWC-DB infoSuper Wide Column DatabaseTransbase
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