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DBMS > Greenplum vs. GridDB vs. Kdb vs. Kinetica

System Properties Comparison Greenplum vs. GridDB vs. Kdb vs. Kinetica

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Editorial information provided by DB-Engines
NameGreenplum  Xexclude from comparisonGridDB  Xexclude from comparisonKdb  Xexclude from comparisonKinetica  Xexclude from comparison
DescriptionAnalytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database infoA logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.Scalable in-memory time series database optimized for IoT and Big DataHigh performance Time Series DBMSFully vectorized database across both GPUs and CPUs
Primary database modelRelational DBMSTime Series DBMSTime Series DBMS
Vector DBMS
Relational DBMS
Secondary database modelsDocument store
Spatial DBMS
Key-value store
Relational DBMS
Relational DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score8.37
Rank#48  Overall
#30  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score7.55
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Websitegreenplum.orggriddb.netkx.comwww.kinetica.com
Technical documentationdocs.greenplum.orgdocs.griddb.netcode.kx.comdocs.kinetica.com
DeveloperPivotal Software Inc.Toshiba CorporationKx Systems, a division of First Derivatives plcKinetica
Initial release200520132000 infokdb was released 2000, kdb+ in 20032012
Current release7.0.0, September 20235.1, August 20223.6, May 20187.1, August 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial infofree 32-bit versioncommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++qC, C++
Server operating systemsLinuxLinuxLinux
OS X
Solaris
Windows
Linux
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes
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 infosince Version 4.2noyesno
Secondary indexesyesyesyes infotable attribute 'grouped'yes
SQL infoSupport of SQLyesSQL92, SQL-like TQL (Toshiba Query Language)SQL-like query language (q)SQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC
Java
Perl
Python
R
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesnouser defined functionsuser defined functions
Triggersyesyesyes infowith viewsyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesShardingShardinghorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replicationSource-replica replicationSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno infosimilar paradigm used for internal processingno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesnoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID at container levelnono
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.noyesyesyes infoGPU vRAM or System RAM
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users can be defined per databaserights management via user accountsAccess rights for users and roles on table level
More information provided by the system vendor
GreenplumGridDBKdbKinetica
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Integrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
tick database streaming sensor data massive intelligence applications oil and gas...
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricsGitHub trending repository
» more
kdb+ performance and reliability proven by our customers in critical infrastructure...
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more
upon request
» more

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More resources
GreenplumGridDBKdbKinetica
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