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DBMS > Kinetica vs. OmniSci vs. Vertica

System Properties Comparison Kinetica vs. OmniSci vs. Vertica

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonOmniSci infoFormerly named 'MapD', rebranded to 'OmniSci' in September 2018  Xexclude from comparisonVertica infoVertica Analytics Platform  Xexclude from comparison
DescriptionGPU-accelerated database for real-time analysis of large and streaming datasetsA high performance, in-memory, column-oriented RDBMS, designed to run on GPUsColumnar relational DBMS designed to handle modern analytic workloads, enabling fast query performance
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.53
Rank#199  Overall
#96  Relational DBMS
Score2.08
Rank#105  Overall
#51  Relational DBMS
Score22.51
Rank#29  Overall
#17  Relational DBMS
Websitewww.kinetica.comwww.omnisci.comwww.vertica.com
Technical documentationwww.kinetica.com/­docswww.omnisci.com/­docs/­latestwww.vertica.com/­documentation/­vertica
DeveloperKineticaMapD Technologies, Inc.Vertica / Micro Focus infoprior to that Hewlett Packard
Initial release201220162005
Current release6.0V4Vertica Analytics Platform 9.2.x, March 2019
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; enterprise edition availablecommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoavailable on Amazon AWS and Microsoft Azure
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C++ and CUDA
Server operating systemsLinuxLinuxLinux
Data schemeyesyesyes infoUnstructured data can be stored in specific Flex-Tables
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.nonoyes
Secondary indexesyesnono infoThe concept of 'projections' can be used instead of indexes.
SQL infoSupport of SQLSQL-like DML and DDL statementsyesyes infonearly full SQL99 without support for indexes and triggers
APIs and other access methodsRESTful HTTP API
JDBC
ODBC
Thrift
JDBC
ODBC
Kafka
Proprietary protocol
RESTful HTTP API
ADO.NET
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
All languages supporting JDBC/ODBC/Thrift
Python
C++
Java
Perl
Python
R
Server-side scripts infoStored proceduresuser defined functionsnoyes
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoRound robinSharding infoSegmentation, Projection Segmentation, Hash Segmentation
Replication methods infoMethods for redundantly storing data on multiple nodesMaster-slave replicationMaster-master replicationMaster-master replication infoCluster level and Object level replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infowith Vertica Connector for Hadoop
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
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
User concepts infoAccess controlAccess rights for users and roles on table levelfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
KineticaOmniSci infoFormerly named 'MapD', rebranded to 'OmniSci' in September 2018Vertica infoVertica Analytics Platform
Specific characteristicsColumnar storage, MPP, Hybrid storage model, Aggressive data compression, High availability,...
» more
Competitive advantagesPerformance at exabyte scale, Deploy anywhere, Open source innovation, Analysis in...
» more
Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are available
» more

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More resources
KineticaOmniSci infoFormerly named 'MapD', rebranded to 'OmniSci' in September 2018Vertica infoVertica Analytics Platform
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