DBMS > Faircom EDGE vs. MonetDB vs. Postgres-XL vs. Snowflake vs. Typesense

System Properties Comparison Faircom EDGE vs. MonetDB vs. Postgres-XL vs. Snowflake vs. Typesense

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

A relational database management system that stores data in columns

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Cloud-based data warehousing service for structured and semi-structured data

A typo-tolerant, in-memory search engine optimized for instant search-as-you-type experiences and developer productivity

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Search engine

Secondary database models

Document store
Spatial DBMS

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Trend Chart

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.79
Rank	#210	Overall
	#14	Search engines

Website

www.faircom.com/products/faircom-edge

Technical documentation

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

www.monetdb.org/Documentation

www.postgres-xl.org/documentation

docs.snowflake.net/manuals/index.html

typesense.org/docs

Developer

FairCom Corporation

MonetDB BV

Snowflake Computing Inc.

Initial release

1979

2004

2014

since 2012, originally named StormDB

2014

2015

Current release

V3, October 2020

Dec2023 (11.49), December 2023

10 R1, October 2018

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Mozilla Public License 2.0

Open Source

Mozilla public license

commercial

Open Source

GPL V3

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++

C++

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

hosted

Linux

Data scheme

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

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free

pre-defined schema optional

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

XML type, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

yes

SQL 2003 with some extensions

yes

distributed, parallel query execution

yes

APIs and other access methods

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

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

CLI Client
JDBC
ODBC

RESTful HTTP API

Supported programming languages

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

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

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

JavaScript (Node.js)
Python

.Net

community maintained
Clojure

community maintained
Dart

community maintained
Go

community maintained
Java

community maintained
JavaScript
Perl

community maintained
PHP
Python
Ruby
Rust

community maintained
Swift

community maintained

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes, in SQL, C, R

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding via remote tables

horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

Source-replica replication available in experimental status

yes

Multi-source replication using RAFT

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable 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

MVCC

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.

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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