DBMS > Amazon Redshift vs. Kinetica vs. RDF4J vs. RDFox

System Properties Comparison Amazon Redshift vs. Kinetica vs. RDF4J vs. RDFox

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Fully vectorized database across both GPUs and CPUs

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

High performance knowledge graph and semantic reasoning engine

Primary database model

Relational DBMS

RDF store

Graph DBMS
RDF store

Secondary database models

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	0.22
Rank	#305	Overall
	#25	Graph DBMS
	#15	RDF stores

Website

aws.amazon.com/redshift

www.kinetica.com

rdf4j.org

www.oxfordsemantic.tech

Technical documentation

docs.aws.amazon.com/redshift

docs.kinetica.com

rdf4j.org/documentation

docs.oxfordsemantic.tech

Developer

Amazon (based on PostgreSQL)

Kinetica

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

Oxford Semantic Technologies

Initial release

2012

2004

2017

Current release

7.1, August 2021

6.0, Septermber 2022

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

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

C, C++

Java

C++

Server operating systems

hosted

Linux

Linux
OS X
Unix
Windows

Linux
macOS
Windows

Data scheme

yes

RDF Schemas

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

RESTful HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

Java
PHP
Python

C
Java

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

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

GPU vRAM or System RAM

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Roles, resources, and access types

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