DBMS > Amazon Redshift vs. Atos Standard Common Repository vs. Firebase Realtime Database vs. FoundationDB vs. Google Cloud Bigtable

System Properties Comparison Amazon Redshift vs. Atos Standard Common Repository vs. Firebase Realtime Database vs. FoundationDB vs. Google Cloud Bigtable

Editorial information provided by DB-Engines

Name

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

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the 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-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Ordered key-value store. Core features are complimented by layers.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Primary database model

Relational DBMS

Document store
Key-value store

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Key-value store
Wide column store

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	1.06
Rank	#185	Overall
	#31	Document stores
	#28	Key-value stores
	#85	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Website

aws.amazon.com/redshift

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

firebase.google.com/products/realtime-database

github.com/apple/foundationdb

cloud.google.com/bigtable

Technical documentation

docs.aws.amazon.com/redshift

firebase.google.com/docs/database

apple.github.io/foundationdb

cloud.google.com/bigtable/docs

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

Google

acquired by Google 2014

FoundationDB

Google

Initial release

2012

2016

2012

2013

2015

Current release

1703

6.2.28, November 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

C++

Server operating systems

hosted

Linux

hosted

Linux
OS X
Windows

hosted

Data scheme

yes

Schema and schema-less with LDAP views

schema-free

some layers support schemas

schema-free

Typing

predefined data types such as float or date

yes

optional

yes

some layers support typing

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

supported in specific SQL layer only

APIs and other access methods

JDBC
ODBC

LDAP

Android
iOS
JavaScript API
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

Java
JavaScript
Objective-C

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

C#
C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

limited functionality with using 'rules'

in SQL-layer only

Triggers

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Linearizable consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

in SQL-layer only

Transaction concepts

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

ACID

Atomic execution of specific operations

yes

ACID

Atomic single-row 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

yes, based on authentication and database rules

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.