DBMS > FatDB vs. Google Cloud Datastore vs. IBM Cloudant vs. RDF4J vs. SwayDB

System Properties Comparison FatDB vs. Google Cloud Datastore vs. IBM Cloudant vs. RDF4J vs. SwayDB

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

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Database as a Service offering based on Apache CouchDB

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Key-value store

Document store

RDF store

Key-value store

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

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	2.85
Rank	#95	Overall
	#18	Document stores

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	0.00
Rank	#384	Overall
	#60	Key-value stores

Website

cloud.google.com/datastore

www.ibm.com/products/cloudant

rdf4j.org

swaydb.simer.au

Technical documentation

cloud.google.com/datastore/docs

cloud.ibm.com/docs/Cloudant

rdf4j.org/documentation

Developer

FatCloud

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

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

Simer Plaha

Initial release

2012

2008

2010

2004

2018

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GNU Affero GPL V3.0

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

Erlang

Java

Scala

Server operating systems

Windows

hosted

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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

Via inetgration in SQL Server

SQL-like query language (GQL)

APIs and other access methods

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

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

RESTful HTTP/JSON API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

Java
PHP
Python

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

via applications

using Google App Engine

View functions (Map-Reduce) in JavaScript

yes

Triggers

yes

via applications

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication using Paxos

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

atomic operations within a document possible

ACID

Isolation support depends on the API used

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Can implement custom security layer via applications

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

Access rights for users can be defined per database

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