DBMS > Amazon DynamoDB vs. CouchDB vs. Microsoft Azure Table Storage vs. MonetDB

System Properties Comparison Amazon DynamoDB vs. CouchDB vs. Microsoft Azure Table Storage vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

A Wide Column Store for rapid development using massive semi-structured datasets

A relational database management system that stores data in columns

Primary database model

Document store
Key-value store

Document store

Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

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

Score	77.57
Rank	#16	Overall
	#2	Document stores
	#2	Key-value stores

Score	10.26
Rank	#45	Overall
	#7	Document stores

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	1.72
Rank	#148	Overall
	#68	Relational DBMS

Website

aws.amazon.com/dynamodb

couchdb.apache.org

azure.microsoft.com/en-us/services/storage/tables

www.monetdb.org

Technical documentation

docs.aws.amazon.com/dynamodb

docs.couchdb.org/en/stable

www.monetdb.org/Documentation

Developer

Amazon

Apache Software Foundation

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

Microsoft

MonetDB BV

Initial release

2012

2005

2012

2004

Current release

3.3.3, December 2023

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache version 2

commercial

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

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Implementation language

Erlang

Server operating systems

hosted

Android
BSD
Linux
OS X
Solaris
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

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.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

View functions in JavaScript

yes, in SQL, C, R

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

Implicit feature of the cloud service

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

atomic operations within a single document possible

optimistic locking

ACID

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.

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users can be defined per database

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

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