DBMS > Amazon DocumentDB vs. BigObject vs. Microsoft Azure Cosmos DB vs. RDF4J

System Properties Comparison Amazon DocumentDB vs. BigObject vs. Microsoft Azure Cosmos DB vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Analytic DBMS for real-time computations and queries

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Document store

Relational DBMS

a hierachical model (tree) can be imposed

Document store
Graph DBMS
Key-value store
Wide column store

RDF store

Secondary database models

Spatial DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

aws.amazon.com/documentdb

bigobject.io

azure.microsoft.com/services/cosmos-db

rdf4j.org

Technical documentation

aws.amazon.com/documentdb/resources

docs.bigobject.io

learn.microsoft.com/azure/cosmos-db

rdf4j.org/documentation

Developer

BigObject, Inc.

Microsoft

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

Initial release

2019

2015

2014

2004

License

Commercial or Open Source

commercial

free community edition available

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

Java

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

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

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

fluentd
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Java
PHP
Python

Server-side scripts

Stored procedures

Lua

JavaScript

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

none

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

automatically between fact table and dimension tables

Transaction concepts

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

Atomic single-document operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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.

yes

User concepts

Access control

Access rights for users and roles

Access rights can be defined down to the item level

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