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DBMS > Kinetica vs. MongoDB vs. OmniSci

System Properties Comparison Kinetica vs. MongoDB vs. OmniSci

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMongoDB  Xexclude from comparisonOmniSci infoFormerly named 'MapD', rebranded to 'OmniSci' in September 2018  Xexclude from comparison
DescriptionGPU-accelerated database for real-time analysis of large and streaming datasetsOne of the most popular document storesA high performance, in-memory, column-oriented RDBMS, designed to run on GPUs
Primary database modelRelational DBMSDocument storeRelational DBMS
Secondary database modelsKey-value storeKey-value storeKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.46
Rank#196  Overall
#96  Relational DBMS
Score387.18
Rank#5  Overall
#1  Document stores
Score2.33
Rank#95  Overall
#48  Relational DBMS
Websitewww.kinetica.comwww.mongodb.comwww.omnisci.com
Technical documentationwww.kinetica.com/­docsdocs.mongodb.com/­manualwww.omnisci.com/­docs/­latest
DeveloperKineticaMongoDB, IncMapD Technologies, Inc.
Initial release201220092016
Current release6.04.0.5, December 2018V4
License infoCommercial or Open SourcecommercialOpen Source infoMongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.Open Source infoApache Version 2; enterprise edition available
Cloud-based only infoOnly available as a cloud servicenono infoMongoDB available as DBaaS (MongoDB Atlas)no
Implementation languageC, C++C++C++ and CUDA
Server operating systemsLinuxLinux
OS X
Solaris
Windows
Linux
Data schemeyesschema-free infoAlthough schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.yes
Typing infopredefined data types such as float or dateyesyes infostring, integer, double, decimal, boolean, date, object_id, geospatialyes
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 indexesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsRead-only SQL queries via the MongoDB Connector for BIyes
APIs and other access methodsRESTful HTTP API
JDBC
ODBC
proprietary protocol using JSONThrift
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Actionscript infoinofficial driver
C
C#
C++
Clojure infoinofficial driver
ColdFusion infoinofficial driver
D infoinofficial driver
Dart infoinofficial driver
Delphi infoinofficial driver
Erlang
Go infoinofficial driver
Groovy infoinofficial driver
Haskell
Java
JavaScript
Lisp infoinofficial driver
Lua infoinofficial driver
MatLab infoinofficial driver
Perl
PHP
PowerShell infoinofficial driver
Prolog infoinofficial driver
Python
R infoinofficial driver
Ruby
Scala
Smalltalk infoinofficial driver
All languages supporting JDBC/ODBC/Thrift
Python
Server-side scripts infoStored proceduresuser defined functionsJavaScriptno
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoRound robin
Replication methods infoMethods for redundantly storing data on multiple nodesMaster-slave replicationMaster-slave replicationMaster-master replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Immediate Consistency infocan be individually decided for each write operation
Immediate Consistency
Foreign keys infoReferential integrityyesno infotypically not used, however similar functionality with DBRef possibleno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoMulti-document ACID Transactions with snapshot isolationno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes infooptionalyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMyes infoIn-memory storage engine introduced with MongoDB version 3.2yes
User concepts infoAccess controlAccess rights for users and roles on table levelAccess rights for users and rolesfine grained access rights according to SQL-standard
More information provided by the system vendor
KineticaMongoDBOmniSci infoFormerly named 'MapD', rebranded to 'OmniSci' in September 2018
Specific characteristicsMongoDB is the next-generation database that helps businesses transform their industries...
» more
Competitive advantagesBy offering the best of traditional databases as well as the flexibility, scale,...
» more
Typical application scenariosInternet of Things (Bosch, Silver Spring Networks) Mobile (The Weather Channel, ADP,...
» more
Key customersADP, Adobe, Amadeus, AstraZeneca, Barclays, BBVA, Bond, Bosch, Cisco, CERN, City...
» more
Market metrics40 million downloads (growing at 30 thousand downloads per day). 6,600+ customers....
» more
Licensing and pricing modelsMongoDB database server: Free Software Foundation’s GNU AGPL v3.0. Commercial licenses...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
KineticaMongoDBOmniSci infoFormerly named 'MapD', rebranded to 'OmniSci' in September 2018
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Recent citations in the news

Global GPU Database Market 2025 Outlook By Kinetica, Omnisci, Sqream, Neo4j, Nvidia, Brytlyt, Jedox, Blazegraph, Blazingdb, Zilliz, Heterodb, H2o.Ai, Fastdata.Io, Fuzzy Logix, Graphistry, Anaconda
26 December 2018, openPR

GPU Database Market Overview and Growth Rate Analysis 2018-2023 by Leading Companies: Kinetica, Neo4j, Nvidia, Brytlyt, Omnisci, Sqream
15 January 2019, openPR

Kinetica Delivers GPU-Accelerated Analytics Engine to SoftBank
1 November 2018, Business Wire

GPU Database Market Expected to Reach $455 Million by 2023 - Press Release
27 December 2018, Digital Journal

Kinetica Could Boost Nvidia In $70B Big Data Market
12 October 2018, Forbes

provided by Google News

Amazon faces strong competition from MongoDB in cloud-based database race
19 January 2019, Fox Business

MongoDB "open-source" Server Side Public License rejected
16 January 2019, ZDNet

Red Hat gets heebie-jeebies over MongoDB's T&Cs squeeze: NoSQL database dropped from RHEL 8B over license
17 January 2019, The Register

Fedora, a Red Hat sponsored project, rejects MongoDB because of SSPL
18 January 2019, Business Insider

MongoDB’s licensing changes led Red Hat to drop the database from the latest version of its server OS
16 January 2019, GeekWire

provided by Google News

Global GPU Database Market 2025 Outlook By Kinetica, Omnisci, Sqream, Neo4j, Nvidia, Brytlyt, Jedox, Blazegraph, Blazingdb, Zilliz, Heterodb, H2o.Ai, Fastdata.Io, Fuzzy Logix, Graphistry, Anaconda
26 December 2018, openPR

GPU Database Market Overview and Growth Rate Analysis 2018-2023 by Leading Companies: Kinetica, Neo4j, Nvidia, Brytlyt, Omnisci, Sqream
15 January 2019, openPR

OmniSci, formerly MapD, gets $55 million in series C funding
4 October 2018, Packt Hub

GPU Database Market Expected to Reach $455 Million by 2023 - Press Release
27 December 2018, Digital Journal

Doing a reality check on GPU-accelerated databases
9 November 2018, SiliconANGLE News

provided by Google News

Job opportunities

Product Management Lead - Databases
Kinetica DB, Arlington, VA

Senior Solutions Engineer (East Coast)
Kinetica DB, New York, NY

Senior Solutions Engineer (West Coast)
Kinetica DB, San Francisco, CA

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