DBMS > Amazon Redshift vs. Faircom EDGE vs. HBase vs. jBASE

System Properties Comparison Amazon Redshift vs. Faircom EDGE vs. HBase vs. jBASE

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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

Wide-column store based on Apache Hadoop and on concepts of BigTable

A robust multi-value DBMS comprising development tools and middleware

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Wide column store

Multivalue DBMS

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	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

Website

aws.amazon.com/redshift

www.faircom.com/products/faircom-edge

hbase.apache.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

Technical documentation

docs.aws.amazon.com/redshift

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

hbase.apache.org/book.html

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

Developer

Amazon (based on PostgreSQL)

FairCom Corporation

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Rocket Software (formerly Zumasys)

Initial release

2012

1979

2008

1991

Current release

V3, October 2020

2.3.4, January 2021

5.7

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache version 2

commercial

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++

Java

Server operating systems

hosted

Android
Linux

ARM, x86
Raspbian
Windows

Linux
Unix
Windows

using Cygwin

AIX
Linux
Windows

Data scheme

yes

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

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

options to bring your own types, AVRO

optional

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

ANSI SQL queries

Embedded SQL for jBASE in BASIC

APIs and other access methods

JDBC
ODBC

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

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
Basic
Jabbascript
Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

.Net, JavaScript, C/C++

yes

Coprocessors in Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

when using SQL

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

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 access rights according to SQL-standard

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights can be defined down to the item level

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