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

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

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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

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

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Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.90
Rank	#134	Overall
	#62	Relational DBMS

Website

aws.amazon.com/redshift

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

www.firebolt.io

Technical documentation

docs.aws.amazon.com/redshift

docs.firebolt.io

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

Firebolt Analytics Inc.

Initial release

2012

2016

2020

Current release

1703

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

Java

Server operating systems

hosted

Linux

hosted

Data scheme

yes

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

optional

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

APIs and other access methods

JDBC
ODBC

LDAP

.Net
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

Go
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on storage layer

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

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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