DBMS > Amazon Redshift vs. Atos Standard Common Repository vs. Google Cloud Firestore vs. JaguarDB vs. MonetDB

System Properties Comparison Amazon Redshift vs. Atos Standard Common Repository vs. Google Cloud Firestore vs. JaguarDB vs. MonetDB

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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Performant, highly scalable DBMS for AI and IoT applications

A relational database management system that stores data in columns

Primary database model

Relational DBMS

Document store
Key-value store

Document store

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Website

aws.amazon.com/redshift

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

firebase.google.com/products/firestore

www.jaguardb.com

www.monetdb.org

Technical documentation

docs.aws.amazon.com/redshift

firebase.google.com/docs/firestore

www.jaguardb.com/support.html

www.monetdb.org/Documentation

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

Google

DataJaguar, Inc.

MonetDB BV

Initial release

2012

2016

2017

2015

2004

Current release

1703

3.3 July 2023

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

Open Source

Mozilla Public License 2.0

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

C++

the server part. Clients available in other languages

Server operating systems

hosted

Linux

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

Schema and schema-less with LDAP views

schema-free

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL 2003 with some extensions

APIs and other access methods

JDBC
ODBC

LDAP

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes, Firebase Rules & Cloud Functions

yes, in SQL, C, R

Triggers

yes

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

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

yes

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

LDAP bind authentication

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

rights management via user accounts

fine grained access rights according to SQL-standard

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