DBMS > Faircom EDGE vs. Ignite vs. JaguarDB vs. Stardog

System Properties Comparison Faircom EDGE vs. Ignite vs. JaguarDB vs. Stardog

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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

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Performant, highly scalable DBMS for AI and IoT applications

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Key-value store
Relational DBMS

Key-value store
Vector DBMS

Graph DBMS
RDF store

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

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	3.11
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.faircom.com/products/faircom-edge

ignite.apache.org

www.jaguardb.com

www.stardog.com

Technical documentation

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

apacheignite.readme.io/docs

www.jaguardb.com/support.html

docs.stardog.com

Developer

FairCom Corporation

Apache Software Foundation

DataJaguar, Inc.

Stardog-Union

Initial release

1979

2015

2010

Current release

V3, October 2020

Apache Ignite 2.6

3.3 July 2023

7.3.0, May 2020

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

GPL V3.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

ANSI C, C++

C++, Java, .Net

C++

the server part. Clients available in other languages

Java

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Solaris
Windows

Linux

Linux
macOS
Windows

Data scheme

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

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

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.

yes

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

ANSI SQL queries

ANSI-99 for query and DML statements, subset of DDL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

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

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes (compute grid and cache interceptors can be used instead)

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

yes (cache interceptors and events)

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes (replicated cache)

Multi-source replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

when using SQL

yes

relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

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

Security Hooks for custom implementations

rights management via user accounts

Access rights for users and roles

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