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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
Score0.57
Rank#188  Overall
#92  Relational DBMS
Score2.53
Rank#91  Overall
#46  Relational DBMS
Score22.07
Rank#28  Overall
#16  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 9.0, September 2017
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 customersAuckland Transport, AOL, Anritsu, Avito, Bla Bla Car, Cardlytics, Cerner, Conservation...
» more
Licensing and pricing modelsCost-based models and subscription-based models are available
» 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
KineticaOmniSci infoFormerly named 'MapD', rebranded to 'OmniSci' in September 2018Vertica infoVertica Analytics Platform
Recent citations in the news

GTC 2019 Bits | StorageReview.com
20 March 2019, StorageReview.com

8 GPU-powered startups on display at Nvidia’s GTC event
15 March 2019, VentureBeat

Kinetica Launches Industry-First Active Analytics Platform
14 March 2019, MarTech Series

Stretching GPU Database Performance With Flash Arrays
26 February 2019, The Next Platform

Kinetica Launches Active Analytics Platform
16 March 2019, insideBIGDATA

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22 February 2019, TechTarget

GPU Database Market 2019 Share, Global Overview, Business Growth, Sales Revenue, Competitive Landscape and Industry Expansion Strategies 2023 - Press Release
28 February 2019, Digital Journal

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

Increasing Data Generation by Various Sectors is a Major Factor Expected to Drive Growth of the Global Graphics Processing Unit Database Market - Media Releases
12 March 2019, CSO Australia

provided by Google News

GoodData Expands Its Open Cloud Strategy with Vertica Partnership
12 March 2019, GlobeNewswire

How Micro Focus Is Fulfilling Its Ambition To Grow Big Data And ML Services In India
8 March 2019, Analytics India Magazine

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21 March 2019, TechTarget

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21 March 2019, Speaking Times

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