DBMS > BigObject vs. Linter vs. RDF4J vs. Ultipa

System Properties Comparison BigObject vs. Linter vs. RDF4J vs. Ultipa

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

RDBMS for high security requirements

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

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

RDF store

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.13
Rank	#335	Overall
	#31	Graph DBMS

Website

Technical documentation

docs.bigobject.io

rdf4j.org/documentation

www.ultipa.com/document

Developer

BigObject, Inc.

relex.ru

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

Ultipa

Initial release

2015

1990

2004

2019

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

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

C and C++

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
OS X
Unix
Windows

Data scheme

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

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

RESTful HTTP API

Supported programming languages

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Java
PHP
Python

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

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

fine grained access rights according to SQL-standard

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