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DBMS > FileMaker vs. Kinetica vs. Spark SQL vs. Tkrzw

System Properties Comparison FileMaker vs. Kinetica vs. Spark SQL vs. Tkrzw

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Editorial information provided by DB-Engines
NameFileMaker  Xexclude from comparisonKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionFileMaker is a cross-platform RDBMS that includes a GUI frontend.Fully vectorized database across both GPUs and CPUsSpark SQL is a component on top of 'Spark Core' for structured data processingA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelRelational DBMSRelational DBMSRelational DBMSKey-value store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score48.20
Rank#20  Overall
#14  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitewww.claris.com/­filemakerwww.kinetica.comspark.apache.org/­sqldbmx.net/­tkrzw
Technical documentationwww.claris.com/­resources/­documentationdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperClaris infoa subsidiary of AppleKineticaApache Software FoundationMikio Hirabayashi
Initial release1983201220142020
Current release19.4.1, November 20217.1, August 20213.5.0 ( 2.13), September 20230.9.3, August 2020
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open Source infoApache 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++
Server operating systemsiOS infoclient part only
Linux
OS X
Windows
LinuxLinux
OS X
Windows
Linux
macOS
Data schemeyesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesno
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.yes infoA http query request can return the data in XML formatnonono
Secondary indexesyesyesno
SQL infoSupport of SQLyes infovia pluginsSQL-like DML and DDL statementsSQL-like DML and DDL statementsno
APIs and other access methodsFilemaker WebDirect
JDBC
ODBC
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Supported programming languagesPHPC++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresyesuser defined functionsnono
Triggersyesyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication, warm standby infosince Version 14Source-replica replicationnonenone
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 Consistency
Foreign keys infoReferential integrityyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
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 infousing specific database classes
User concepts infoAccess controlsimple rights management via user accounts and connection to external directory servicesAccess rights for users and roles on table levelnono

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FileMakerKineticaSpark SQLTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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