DBMS > Amazon DynamoDB vs. EsgynDB vs. FatDB vs. RDF4J

System Properties Comparison Amazon DynamoDB vs. EsgynDB vs. FatDB vs. RDF4J

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Document store
Key-value store

Relational DBMS

Document store
Key-value store

RDF store

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

aws.amazon.com/dynamodb

www.esgyn.cn

rdf4j.org

Technical documentation

docs.aws.amazon.com/dynamodb

rdf4j.org/documentation

Developer

Amazon

Esgyn

FatCloud

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2012

2015

2012

2004

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C++, Java

Java

Server operating systems

hosted

Linux

Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

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

SQL

Support of SQL

yes

Via inetgration in SQL Server

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

Java
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes

via applications

yes

Triggers

yes

by integration with AWS Lambda

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

selectable replication factor

none

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

Immediate Consistency

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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)

fine grained access rights according to SQL-standard

Can implement custom security layer via applications

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