DBMS > Amazon DynamoDB vs. Microsoft Azure Table Storage vs. OpenQM vs. Realm vs. Vitess

System Properties Comparison Amazon DynamoDB vs. Microsoft Azure Table Storage vs. OpenQM vs. Realm vs. Vitess

Editorial information provided by DB-Engines

Name

Description

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

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

QpenQM is a high-performance, self-tuning, multi-value DBMS

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store
Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

aws.amazon.com/dynamodb

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

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

realm.io

vitess.io

Technical documentation

docs.aws.amazon.com/dynamodb

realm.io/docs

vitess.io/docs

Developer

Amazon

Microsoft

Rocket Software, originally Martin Phillips

Realm, acquired by MongoDB in May 2019

The Linux Foundation, PlanetScale

Initial release

2012

1993

2014

2013

Current release

3.4-12

15.0.2, December 2022

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

GPLv2, extended commercial license available

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Server operating systems

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Android
Backend: server-less
iOS
Windows

Docker
Linux
macOS

Data scheme

schema-free

yes

with some exceptions

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

.Net
Basic
C
Java
Objective C
PHP
Python

.Net
Java

with Android only
Objective-C
React Native
Swift

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

runs within the applications so server-side scripts are unnecessary

yes

proprietary syntax

Triggers

yes

by integration with AWS Lambda

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

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

optimistic locking

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

In-Memory realm

yes

User concepts

Access control

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

Access rights based on private key authentication or shared access signatures

Access rights can be defined down to the item level

yes

Users with fine-grained authorization concept

no user groups or roles

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