DBMS > Amazon Neptune vs. BigObject vs. Citus vs. Faircom DB vs. Warp 10

System Properties Comparison Amazon Neptune vs. BigObject vs. Citus vs. Faircom DB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Analytic DBMS for real-time computations and queries

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Graph DBMS
RDF store

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	2.20
Rank	#113	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	2.13
Rank	#117	Overall
	#56	Relational DBMS

Score	0.18
Rank	#314	Overall
	#45	Key-value stores
	#141	Relational DBMS

Score	0.06
Rank	#351	Overall
	#35	Time Series DBMS

Website

aws.amazon.com/neptune

bigobject.io

www.citusdata.com

www.faircom.com/products/faircom-db

www.warp10.io

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.bigobject.io

docs.citusdata.com

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

www.warp10.io/content/02_Getting_started

Developer

Amazon

BigObject, Inc.

FairCom Corporation

SenX

Initial release

2017

2015

2010

1979

2015

Current release

8.1, December 2018

V13, July 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

AGPL, commercial license also available

commercial

Restricted, free version available

Open Source

Apache License 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++

Java

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
OS X
Windows

Data scheme

schema-free

yes

schema free, schema optional, schema required, partial schema,

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

standard, with numerous extensions

yes, ANSI SQL with proprietary extensions

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

fluentd
ODBC
RESTful HTTP API

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

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

HTTP API
Jupyter
WebSocket

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Server-side scripts

Stored procedures

Lua

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

.Net, JavaScript, C/C++

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

based on HBase

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.

none

Source-replica replication

other methods possible by using 3rd party extensions

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

Yes, tunable from durable to delayed durability to in-memory

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 access rights according to SQL-standard

Fine grained access rights according to SQL-standard with additional protections for files

Mandatory use of cryptographic tokens, containing fine-grained authorizations

More information provided by the system vendor

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