DBMS > Faircom EDGE vs. FatDB vs. Hive vs. TimesTen

System Properties Comparison Faircom EDGE vs. FatDB vs. Hive vs. TimesTen

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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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

In-Memory RDBMS compatible to Oracle

Primary database model

Key-value store
Relational DBMS

Document store
Key-value store

Relational 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	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

www.faircom.com/products/faircom-edge

hive.apache.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

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

cwiki.apache.org/confluence/display/Hive/Home

docs.oracle.com/database/timesten-18.1

Developer

FairCom Corporation

FatCloud

Apache Software Foundation

initially developed by Facebook

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

1979

2012

1998

Current release

V3, October 2020

3.1.3, April 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2

commercial

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

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Windows

All OS with a Java VM

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
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

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

Via inetgration in SQL Server

SQL-like DML and DDL statements

yes

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

JDBC
ODBC
Thrift

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

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

C++
Java
PHP
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

via applications

yes

user defined functions and integration of map-reduce

PL/SQL

Triggers

yes

via applications

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

when using SQL

yes

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

by means of logfiles and checkpoints

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

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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More resources

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