DBMS > Faircom EDGE vs. Fujitsu Enterprise Postgres vs. PouchDB vs. Stardog

System Properties Comparison Faircom EDGE vs. Fujitsu Enterprise Postgres vs. PouchDB 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

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

JavaScript DBMS with an API inspired by CouchDB

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

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Document store

Graph DBMS
RDF store

Secondary database models

Document store
Spatial DBMS

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	0.22
Rank	#304	Overall
	#137	Relational DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Website

www.faircom.com/products/faircom-edge

www.postgresql.fastware.com

pouchdb.com

www.stardog.com

Technical documentation

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

www.postgresql.fastware.com/product-manuals

pouchdb.com/guides

docs.stardog.com

Developer

FairCom Corporation

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Apache Software Foundation

Stardog-Union

Initial release

1979

2012

2010

Current release

V3, October 2020

Fujitsu Enterprise Postgres 14, January 2022

7.1.1, June 2019

7.3.0, May 2020

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

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

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Implementation language

ANSI C, C++

JavaScript

Java

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Linux
macOS
Windows

Data scheme

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

yes

schema-free

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

via views

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

ANSI SQL queries

yes

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

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP REST

only for PouchDB Server
JavaScript API

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

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

JavaScript

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

View functions in JavaScript

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

partitioning by range, list and by hash

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Multi-source replication in HA-Cluster

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

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, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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.

fine grained access rights according to SQL-standard

Access rights for users and roles

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