DBMS > atoti vs. RDF4J vs. Transbase

System Properties Comparison atoti vs. RDF4J vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Object oriented DBMS

RDF store

Relational DBMS

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

Score	0.48
Rank	#248	Overall
	#11	Object oriented DBMS

Score	0.83
Rank	#202	Overall
	#7	RDF stores

Score	0.00
Rank	#379	Overall
	#155	Relational DBMS

Website

atoti.io

rdf4j.org

www.transaction.de/en/products/transbase.html

Technical documentation

docs.atoti.io

rdf4j.org/documentation

www.transaction.de/en/products/transbase/features.html

Developer

ActiveViam

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

Transaction Software GmbH

Initial release

2004

1987

Current release

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

free versions available

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

free development license

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Server operating systems

Linux
OS X
Unix
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

RDF Schemas

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

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java
PHP
Python

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

yes

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

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