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DBMS > FatDB vs. Kinetica vs. MonetDB vs. RavenDB vs. Spark SQL

System Properties Comparison FatDB vs. Kinetica vs. MonetDB vs. RavenDB vs. Spark SQL

Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonKinetica  Xexclude from comparisonMonetDB  Xexclude from comparisonRavenDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.Fully vectorized database across both GPUs and CPUsA relational database management system that stores data in columnsOpen Source Operational and Transactional Enterprise NoSQL Document DatabaseSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store
Key-value store
Relational DBMSRelational DBMSDocument storeRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store
Spatial DBMS
Graph DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score1.72
Rank#145  Overall
#67  Relational DBMS
Score2.92
Rank#101  Overall
#18  Document stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.kinetica.comwww.monetdb.orgravendb.netspark.apache.org/­sql
Technical documentationdocs.kinetica.comwww.monetdb.org/­Documentationravendb.net/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFatCloudKineticaMonetDB BVHibernating RhinosApache Software Foundation
Initial release20122012200420102014
Current release7.1, August 2021Dec2023 (11.49), December 20235.4, July 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoMozilla Public License 2.0Open Source infoAGPL version 3, commercial license availableOpen Source infoApache 2.0
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 languageC#C, C++CC#Scala
Server operating systemsWindowsLinuxFreeBSD
Linux
OS X
Solaris
Windows
Linux
macOS
Raspberry Pi
Windows
Linux
OS X
Windows
Data schemeschema-freeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesnoyes
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 indexesyesyesyesyesno
SQL infoSupport of SQLno infoVia inetgration in SQL ServerSQL-like DML and DDL statementsyes infoSQL 2003 with some extensionsSQL-like query language (RQL)SQL-like DML and DDL statements
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
JDBC
ODBC
RESTful HTTP API
JDBC
native C library infoMAPI library (MonetDB application programming interface)
ODBC
.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
JDBC
ODBC
Supported programming languagesC#C++
Java
JavaScript (Node.js)
Python
C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infovia applicationsuser defined functionsyes, in SQL, C, Ryesno
Triggersyes infovia applicationsyes infotriggers when inserted values for one or more columns fall within a specified rangeyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding via remote tablesShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationnone infoSource-replica replication available in experimental statusMulti-source replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency or Eventual Consistency depending on configurationDefault ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.
Foreign keys infoReferential integritynoyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACID, Cluster-wide transaction availableno
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.yes infoGPU vRAM or System RAMno
User concepts infoAccess controlno infoCan implement custom security layer via applicationsAccess rights for users and roles on table levelfine grained access rights according to SQL-standardAuthorization levels configured per client per databaseno

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More resources
FatDBKineticaMonetDBRavenDBSpark SQL
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