DBMS > Faircom EDGE vs. RDF4J vs. Tibero vs. XTDB

System Properties Comparison Faircom EDGE vs. RDF4J vs. Tibero vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

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

A secure RDBMS, designed for easy portability from Oracle

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store
Relational DBMS

RDF store

Relational DBMS

Document store

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

Score	0.02
Rank	#370	Overall
	#55	Key-value stores
	#157	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	1.46
Rank	#151	Overall
	#70	Relational DBMS

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

www.faircom.com/products/faircom-edge

rdf4j.org

us.tmaxsoft.com/products/tibero

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

rdf4j.org/documentation

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

www.xtdb.com/docs

Developer

FairCom Corporation

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

TmaxSoft

Juxt Ltd.

Initial release

1979

2004

2003

2019

Current release

V3, October 2020

7, September 2022

1.19, September 2021

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

Java

C and Assembler

Clojure

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

RDF Schemas

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

yes, extensible-data-notation format

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

HTTP REST
JDBC

Supported programming languages

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Java
PHP
Python

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Clojure
Java

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Persistent Stored Procedure (PSM)

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

none

horizontal partitioning

by range, hash, list or composite

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

Multi-source replication
Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

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

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

planned for next version

User concepts

Access control

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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