DBMS > Faircom EDGE vs. Hive vs. MonetDB vs. Trafodion

System Properties Comparison Faircom EDGE vs. Hive vs. MonetDB vs. Trafodion

Please select another system to include it in the comparison.

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

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

A relational database management system that stores data in columns

Transactional SQL-on-Hadoop DBMS

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.04
Rank	#338	Overall
	#50	Key-value stores
	#151	Relational DBMS

Score	78.66
Rank	#17	Overall
	#11	Relational DBMS

Score	1.79
Rank	#129	Overall
	#63	Relational DBMS

Score	0.46
Rank	#246	Overall
	#118	Relational DBMS

Website

www.faircom.com/products/faircomedge-iot-database

hive.apache.org

www.monetdb.org

trafodion.apache.org

Technical documentation

learn.faircomcorp.com/developers/documentation_directory

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

www.monetdb.org/Documentation

trafodion.apache.org

Developer

FairCom Corporation

Apache Software Foundation

initially developed by Facebook

MonetDB BV

Apache Software Foundation, originally developed by HP

Initial release

1979

2012

2004

2014

Current release

V3, October 2020

3.1.2, August 2019

Jul2021-SP2, July 2021

2.3.0, February 2019

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2

Open Source

Mozilla Public License 2.0

Open Source

Apache 2.0

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++

Java

C++, Java

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

Flexible Schema (defined schema, partial schema, 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

SQL-like DML and DDL statements

yes

SQL 2003 with some extensions

yes

APIs and other access methods

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

JDBC
ODBC
Thrift

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

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

C++
Java
PHP
Python

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

user defined functions and integration of map-reduce

yes, in SQL, C, R

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

none

Source-replica replication available in experimental status

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via user defined functions and HBase

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

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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.

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Column-store In-memory optimized GeoSpatial support JSON document support
» more

Competitive advantages

Scalable architecture - vertical data organization and in-memory optimization guarantee...
» more

Typical application scenarios

Analytical database, Data warehouse, OLAP Data mining Scientific database
» more

Key customers

Numascale , Spinque , SecurActive , CHS , NOZHUP
» more

Licensing and pricing models

Free and open-source software, Mozilla Public License 2.0 Commercial support available
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.