DBMS > Faircom EDGE vs. FatDB vs. Firebird vs. InfinityDB vs. JaguarDB

System Properties Comparison Faircom EDGE vs. FatDB vs. Firebird vs. InfinityDB vs. JaguarDB

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Firebird is an open source RDBMS forked from Borland's InterBase

A Java embedded Key-Value Store which extends the Java Map interface

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Key-value store
Relational DBMS

Document store
Key-value store

Relational DBMS

Key-value store

Key-value store
Vector DBMS

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

www.faircom.com/products/faircom-edge

www.firebirdsql.org

boilerbay.com

www.jaguardb.com

Technical documentation

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

www.firebirdsql.org/en/reference-manuals

boilerbay.com/infinitydb/manual

www.jaguardb.com/support.html

Developer

FairCom Corporation

FatCloud

Firebird Foundation

Boiler Bay Inc.

DataJaguar, Inc.

Initial release

1979

2012

2000

As fork of Borland's InterBase

2002

2015

Current release

V3, October 2020

5.0.0, January 2024

4.0

3.3 July 2023

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Initial Developer's Public License

commercial

Open Source

GPL V3.0

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 and C++

Java

C++

the server part. Clients available in other languages

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Windows

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

All OS with a Java VM

Linux

Data scheme

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

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

Secondary indexes

yes

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

ANSI SQL queries

Via inetgration in SQL Server

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

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

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
ODBC

Supported programming languages

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

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

Java

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

via applications

PSQL

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

Source-replica replication

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Eventual Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Features a multi-generational MVCC architecture, readers do not block writers

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.

Can implement custom security layer via applications

Users with fine-grained authorization concept

rights management via user accounts

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