DBMS > Amazon Redshift vs. Atos Standard Common Repository vs. Faircom EDGE vs. Kinetica

System Properties Comparison Amazon Redshift vs. Atos Standard Common Repository vs. Faircom EDGE vs. Kinetica

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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

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Fully vectorized database across both GPUs and CPUs

Primary database model

Relational DBMS

Document store
Key-value store

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Website

aws.amazon.com/redshift

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

www.faircom.com/products/faircom-edge

www.kinetica.com

Technical documentation

docs.aws.amazon.com/redshift

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

docs.kinetica.com

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

FairCom Corporation

Kinetica

Initial release

2012

2016

1979

2012

Current release

1703

V3, October 2020

7.1, August 2021

License

Commercial or Open Source

commercial

Restricted, free version available

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

Java

ANSI C, C++

C, C++

Server operating systems

hosted

Linux

Android
Linux

ARM, x86
Raspbian
Windows

Linux

Data scheme

yes

Schema and schema-less with LDAP views

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

optional

yes, ANSI Standard SQL Types

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 queries

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

LDAP

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

.Net, JavaScript, C/C++

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

cell division

File partitioning

Customizable business rules for partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

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

Immediate Consistency
Tunable Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

when using SQL

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

LDAP bind authentication

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Access rights for users and roles on table level

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