DBMS > Couchbase vs. Faircom EDGE vs. FatDB vs. HBase vs. SWC-DB

System Properties Comparison Couchbase vs. Faircom EDGE vs. FatDB vs. HBase vs. SWC-DB

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

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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.

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

A high performance, scalable Wide Column DBMS

Primary database model

Document store

Key-value store
Relational DBMS

Document store
Key-value store

Wide column store

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Time Series DBMS

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.couchbase.com

www.faircom.com/products/faircom-edge

hbase.apache.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.couchbase.com

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

hbase.apache.org/book.html

Developer

Couchbase, Inc.

FairCom Corporation

FatCloud

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Alex Kashirin

Initial release

2011

1979

2012

2008

2020

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

V3, October 2020

2.3.4, January 2021

0.5, April 2021

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

Restricted, free version available

commercial

Open Source

Apache version 2

Open Source

GPL V3

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

C, C++, Go and Erlang

ANSI C, C++

Java

C++

Server operating systems

Linux
OS X
Windows

Android
Linux

ARM, x86
Raspbian
Windows

Windows

Linux
Unix
Windows

using Cygwin

Linux

Data scheme

schema-free

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

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

options to bring your own types, AVRO

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

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

ANSI SQL queries

Via inetgration in SQL Server

SQL-like query language

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

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

Java API
RESTful HTTP API
Thrift

Proprietary protocol
Thrift

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

.Net, JavaScript, C/C++

yes

via applications

yes

Coprocessors in Java

Triggers

yes

via the TAP protocol

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

File partitioning

Customizable business rules for partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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)

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

Ephemeral buckets

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base 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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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