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DBMS > Kinetica vs. Microsoft Azure SQL Database vs. Spark SQL vs. SWC-DB

System Properties Comparison Kinetica vs. Microsoft Azure SQL Database vs. Spark SQL vs. SWC-DB

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonSpark SQL  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsDatabase as a Service offering with high compatibility to Microsoft SQL ServerSpark SQL is a component on top of 'Spark Core' for structured data processingA high performance, scalable Wide Column DBMS
Primary database modelRelational DBMSRelational DBMSRelational DBMSWide column store
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store
Graph DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.69
Rank#234  Overall
#107  Relational DBMS
Score78.40
Rank#15  Overall
#10  Relational DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score0.01
Rank#387  Overall
#13  Wide column stores
Websitewww.kinetica.comazure.microsoft.com/­en-us/­products/­azure-sql/­databasespark.apache.org/­sqlgithub.com/­kashirin-alex/­swc-db
www.swcdb.org
Technical documentationdocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­azure-sqlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaMicrosoftApache Software FoundationAlex Kashirin
Initial release2012201020142020
Current release7.1, August 2021V123.5.0 ( 2.13), September 20230.5, April 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open Source infoGPL V3
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C++ScalaC++
Server operating systemsLinuxhostedLinux
OS X
Windows
Linux
Data schemeyesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyes
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.noyesnono
Secondary indexesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL-like DML and DDL statementsSQL-like query language
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
JDBC
ODBC
Proprietary protocol
Thrift
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
C++
Server-side scripts infoStored proceduresuser defined functionsTransact SQLnono
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes, with always 3 replicas availablenone
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 integrityyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
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 levelfine grained access rights according to SQL-standardno

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More resources
KineticaMicrosoft Azure SQL Database infoformerly SQL AzureSpark SQLSWC-DB infoSuper Wide Column Database
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