DBMS > Amazon Redshift vs. Linter vs. OushuDB

System Properties Comparison Amazon Redshift vs. Linter vs. OushuDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

RDBMS for high security requirements

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Website

aws.amazon.com/redshift

linter.ru

www.oushu.com/product/oushuDB

Technical documentation

docs.aws.amazon.com/redshift

www.oushu.com/documentation

Developer

Amazon (based on PostgreSQL)

relex.ru

Oushu

Initial release

2012

1990

Current release

4.0.1, August 2020

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Server operating systems

hosted

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

Full-featured ANSI SQL support

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
C++

Server-side scripts

Stored procedures

user defined functions

in Python

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

User concepts

Access control

fine grained access rights according to SQL-standard

Kerberos, SSL and role based access

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