DBMS > Amazon Redshift vs. Atos Standard Common Repository vs. Faircom DB vs. Geode

System Properties Comparison Amazon Redshift vs. Atos Standard Common Repository vs. Faircom DB vs. Geode

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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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Primary database model

Relational DBMS

Document store
Key-value store

Key-value store
Relational DBMS

Key-value store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Website

aws.amazon.com/redshift

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

www.faircom.com/products/faircom-db

geode.apache.org

Technical documentation

docs.aws.amazon.com/redshift

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

geode.apache.org/docs

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

FairCom Corporation

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Initial release

2012

2016

1979

2002

Current release

1703

V12, November 2020

1.1, February 2017

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2; commercial licenses available as Gemfire

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

ANSI C, C++

Java

Server operating systems

hosted

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

All OS with a Java VM

the JDK (8 or later) is also required

Data scheme

yes

Schema and schema-less with LDAP views

schema free, schema optional, schema required, partial schema,

schema-free

Typing

predefined data types such as float or date

yes

optional

yes, ANSI SQL Types, JSON, typed binary structures

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes, ANSI SQL with proprietary extensions

SQL-like query language (OQL)

APIs and other access methods

JDBC
ODBC

LDAP

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Java Client API
Memcached protocol
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
All JVM based languages
C++
Groovy
Java
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

.Net, JavaScript, C/C++

user defined functions

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source 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

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual 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

Atomic execution of specific operations

tunable from ACID to Eventually Consistent

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

Access rights per client and object definable

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