DBMS > Amazon Neptune vs. Faircom EDGE vs. FatDB vs. HBase vs. SQLite

System Properties Comparison Amazon Neptune vs. Faircom EDGE vs. FatDB vs. HBase vs. SQLite

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

Fast, reliable graph database built for the cloud

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

Widely used embeddable, in-process RDBMS

Primary database model

Graph DBMS
RDF store

Key-value store
Relational DBMS

Document store
Key-value store

Wide column store

Relational DBMS

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

Score	2.29
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

aws.amazon.com/neptune

www.faircom.com/products/faircom-edge

hbase.apache.org

www.sqlite.org

Technical documentation

aws.amazon.com/neptune/developer-resources

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

hbase.apache.org/book.html

www.sqlite.org/docs.html

Developer

Amazon

FairCom Corporation

FatCloud

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Dwayne Richard Hipp

Initial release

2017

1979

2012

2008

2000

Current release

V3, October 2020

2.3.4, January 2021

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache version 2

Open Source

Public Domain

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

Windows

Linux
Unix
Windows

using Cygwin

server-less

Data scheme

schema-free

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

schema-free

schema-free, schema definition possible

yes

dynamic column types

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

options to bring your own types, AVRO

yes

not rigid because of 'dynamic typing' concept.

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

Via inetgration in SQL Server

yes

SQL-92 is not fully supported

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

via applications

yes

Coprocessors in Java

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

Multi-source replication
Source-replica replication

none

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

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

when using SQL

yes

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

via file-system locks

Durability

Support for making data persistent

yes

with encyption-at-rest

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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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