DBMS > Altibase vs. Amazon Redshift vs. GBase vs. Kinetica

System Properties Comparison Altibase vs. Amazon Redshift vs. GBase vs. Kinetica

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Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Large scale data warehouse service for use with business intelligence tools

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Fully vectorized database across both GPUs and CPUs

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.87
Rank	#200	Overall
	#93	Relational DBMS

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	1.11
Rank	#173	Overall
	#78	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Website

altibase.com

aws.amazon.com/redshift

www.gbase.cn

www.kinetica.com

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

docs.aws.amazon.com/redshift

docs.kinetica.com

Developer

Altibase

Amazon (based on PostgreSQL)

General Data Technology Co., Ltd.

Kinetica

Initial release

1999

2012

2004

2012

Current release

v7.3, 2023, August 2023

GBase 8a, GBase 8s, GBase 8c

7.1, August 2021

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C, Java, Python

C, C++

Server operating systems

AIX
HP-UX
Linux

hosted

Linux

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.

yes

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

ANSI SQL-92

yes

does not fully support an SQL-standard

Standard with numerous extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Supported programming languages

C
C++
Java

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

stored procedures and stored functions

user defined functions

in Python

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Access rights for users and roles on table level

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