DBMS > EXASOL vs. RDF4J vs. Transbase

System Properties Comparison EXASOL vs. RDF4J vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

Relational DBMS

RDF store

Relational DBMS

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

Score	1.58
Rank	#139	Overall
	#63	Relational DBMS

Score	0.83
Rank	#202	Overall
	#7	RDF stores

Score	0.00
Rank	#379	Overall
	#155	Relational DBMS

Website

www.exasol.com

rdf4j.org

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

Technical documentation

www.exasol.com/resources

rdf4j.org/documentation

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

Developer

Exasol

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

Transaction Software GmbH

Initial release

2000

2004

1987

Current release

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

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

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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

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

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
Lua
Python
R

Java
PHP
Python

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

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

yes

Hadoop integration

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

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

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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