DBMS > Cubrid vs. Datomic vs. DolphinDB vs. Netezza

System Properties Comparison Cubrid vs. Datomic vs. DolphinDB vs. Netezza

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Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Website

cubrid.com (korean)
cubrid.org (english)

www.datomic.com

www.dolphindb.com

www.ibm.com/products/netezza

Technical documentation

cubrid.org/manuals

docs.datomic.com

docs.dolphindb.cn/en/help200/index.html

Developer

CUBRID Corporation, CUBRID Foundation

Cognitect

DolphinDB, Inc

IBM

Initial release

2008

2012

2018

2000

Current release

11.0, January 2021

1.0.6735, June 2023

v2.00.4, January 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

commercial

free community version available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++, Java

Java, Clojure

C++

Server operating systems

Linux
Windows

All OS with a Java VM

Linux
Windows

Linux

included in appliance

Data scheme

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

JDBC
ODBC
OLE DB

Supported programming languages

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Clojure
Java

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Transaction Functions

yes

Triggers

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

But extensive use of caching in the application peers

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Administrators, Users, Groups

Users with fine-grained authorization concept

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