DBMS > Amazon Redshift vs. Faircom EDGE vs. Fauna vs. Ingres vs. Tkrzw

System Properties Comparison Amazon Redshift vs. Faircom EDGE vs. Fauna vs. Ingres vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Well established RDBMS

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Relational DBMS

Key-value store

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	1.55
Rank	#151	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

aws.amazon.com/redshift

www.faircom.com/products/faircom-edge

fauna.com

www.actian.com/databases/ingres

dbmx.net/tkrzw

Technical documentation

docs.aws.amazon.com/redshift

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

docs.fauna.com

docs.actian.com/ingres

Developer

Amazon (based on PostgreSQL)

FairCom Corporation

Fauna, Inc.

Actian Corporation

Mikio Hirabayashi

Initial release

2012

1979

2014

1974

originally developed at University Berkely in early 1970s

2020

Current release

V3, October 2020

11.2, May 2022

0.9.3, August 2020

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

Scala

C++

Server operating systems

hosted

Android
Linux

ARM, x86
Raspbian
Windows

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
macOS

Data scheme

yes

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

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

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

but tools for importing/exporting data from/to XML-files available

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

ANSI SQL queries

yes

APIs and other access methods

JDBC
ODBC

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

RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

Supported programming languages

All languages supporting JDBC/ODBC

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

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

.Net, JavaScript, C/C++

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

horizontal partitioning

consistent hashing

horizontal partitioning

Ingres Star to access multiple databases simultaneously

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication

Ingres Replicator

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

MVCC

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

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Identity management, authentication, and access control

fine grained access rights according to SQL-standard

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