DBMS > Faircom EDGE vs. IBM Cloudant vs. IBM Db2 Event Store vs. PostgreSQL

System Properties Comparison Faircom EDGE vs. IBM Cloudant vs. IBM Db2 Event Store vs. PostgreSQL

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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

Database as a Service offering based on Apache CouchDB

Distributed Event Store optimized for Internet of Things use cases

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Key-value store
Relational DBMS

Document store

Event Store
Time Series DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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	2.68
Rank	#106	Overall
	#20	Document stores

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Website

www.faircom.com/products/faircom-edge

www.ibm.com/products/cloudant

www.ibm.com/products/db2-event-store

www.postgresql.org

Technical documentation

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

cloud.ibm.com/docs/Cloudant

www.ibm.com/docs/en/db2-event-store

www.postgresql.org/docs

Developer

FairCom Corporation

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

IBM

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

1979

2010

2017

1989

1989: Postgres, 1996: PostgreSQL

Current release

V3, October 2020

2.0

16.3, May 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

free developer edition available

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

Erlang

C and C++

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux

Linux, macOS, Windows for the developer addition

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

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

schema-free

yes

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

yes

through the embedded Spark runtime

yes

standard with numerous extensions

APIs and other access methods

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

RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

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

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

View functions (Map-Reduce) in JavaScript

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

partitioning by range, list and (since PostgreSQL 11) by hash

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication
Source-replica replication

Active-active shard replication

Source-replica replication

other methods possible by using 3rd party extensions

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

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Optimistic locking

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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.

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

More information provided by the system vendor

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