DBMS > Amazon Redshift vs. Atos Standard Common Repository vs. atoti

System Properties Comparison Amazon Redshift vs. Atos Standard Common Repository vs. atoti

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Primary database model

Relational DBMS

Document store
Key-value store

Object oriented DBMS

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Trend Chart

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	0.59
Rank	#242	Overall
	#10	Object oriented DBMS

Website

aws.amazon.com/redshift

atos.net/en/convergence-creators/portfolio/standard-common-repository

atoti.io

Technical documentation

docs.aws.amazon.com/redshift

docs.atoti.io

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

ActiveViam

Initial release

2012

2016

Current release

1703

License

Commercial or Open Source

commercial

free versions available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux

Data scheme

yes

Schema and schema-less with LDAP views

Typing

predefined data types such as float or date

yes

optional

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

Multidimensional Expressions (MDX)

APIs and other access methods

JDBC
ODBC

LDAP

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

Server-side scripts

Stored procedures

user defined functions

in Python

Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Transaction concepts

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

ACID

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

LDAP bind authentication

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