DBMS > Faircom EDGE vs. FatDB vs. Hazelcast vs. RRDtool

System Properties Comparison Faircom EDGE vs. FatDB vs. Hazelcast vs. RRDtool

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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.

A widely adopted in-memory data grid

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Key-value store
Relational DBMS

Document store
Key-value store

Key-value store

Time Series DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	0.03
Rank	#380	Overall
	#59	Key-value stores
	#157	Relational DBMS

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	2.14
Rank	#124	Overall
	#10	Time Series DBMS

Website

www.faircom.com/products/faircom-edge

hazelcast.com

oss.oetiker.ch/rrdtool

Technical documentation

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

hazelcast.org/imdg/docs

oss.oetiker.ch/rrdtool/doc

Developer

FairCom Corporation

FatCloud

Hazelcast

Tobias Oetiker

Initial release

1979

2012

2008

1999

Current release

V3, October 2020

5.3.6, November 2023

1.8.0, 2022

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL V2 and FLOSS

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++

Java

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Windows

All OS with a Java VM

HP-UX
Linux

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

Numeric data only

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

the object must implement a serialization strategy

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

Via inetgration in SQL Server

SQL-like query language

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

JCache
JPA
Memcached protocol
RESTful HTTP API

in-process shared library
Pipes

Supported programming languages

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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

via applications

yes

Event Listeners, Executor Services

Triggers

yes

via applications

yes

Events

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

selectable replication factor

yes

Replicated Map

none

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 or Eventual Consistency selectable by user

Raft Consensus Algorithm

none

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

by using the rrdcached daemon

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

Role-based access control

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More resources

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