DBMS > Amazon Redshift vs. CouchDB vs. Firebase Realtime Database vs. Google BigQuery vs. searchxml

System Properties Comparison Amazon Redshift vs. CouchDB vs. Firebase Realtime Database vs. Google BigQuery vs. searchxml

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Large scale data warehouse service with append-only tables

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Relational DBMS

Document store

Relational DBMS

Native XML DBMS
Search engine

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Website

aws.amazon.com/redshift

couchdb.apache.org

firebase.google.com/products/realtime-database

cloud.google.com/bigquery

www.searchxml.net/category/products

Technical documentation

docs.aws.amazon.com/redshift

docs.couchdb.org/en/stable

firebase.google.com/docs/database

cloud.google.com/bigquery/docs

www.searchxml.net/support/handouts

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

acquired by Google 2014

Google

informationpartners gmbh

Initial release

2012

2005

2012

2010

2015

Current release

3.3.3, December 2023

1.0

License

Commercial or Open Source

commercial

Open Source

Apache version 2

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

Erlang

C++

Server operating systems

hosted

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

via views

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

Android
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP/JSON API

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

Java
JavaScript
Objective-C

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions

in Python

View functions in JavaScript

limited functionality with using 'rules'

user defined functions

in JavaScript

yes

on the application server

Triggers

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

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 operations within a single document possible

yes

Since BigQuery is designed for querying data

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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

Access rights for users can be defined per database

yes, based on authentication and database rules

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Domain, group and role-based access control at the document level and for application services

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.