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DBMS > Kinetica vs. Spark SQL

System Properties Comparison Kinetica vs. Spark SQL

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.93
Rank#237  Overall
#109  Relational DBMS
Score20.07
Rank#35  Overall
#21  Relational DBMS
Websitewww.kinetica.comspark.apache.org/­sql
Technical documentationwww.kinetica.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaApache Software Foundation
Initial release20122014
Current release7.1, August 20213.4.0 ( 2.13), April 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++Scala
Server operating systemsLinuxLinux
OS X
Windows
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyes
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 indexesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsno
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users and roles on table levelno
More information provided by the system vendor
KineticaSpark SQL
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...
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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
KineticaSpark SQL
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