DBMS > Amazon Neptune vs. Atos Standard Common Repository vs. Bangdb vs. BigObject

System Properties Comparison Amazon Neptune vs. Atos Standard Common Repository vs. Bangdb vs. BigObject

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Fast, reliable graph database built for the cloud

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Converged and high performance database for device data, events, time series, document and graph

Analytic DBMS for real-time computations and queries

Primary database model

Graph DBMS
RDF store

Document store
Key-value store

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Secondary database models

Spatial DBMS

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Website

aws.amazon.com/neptune

atos.net/en/convergence-creators/portfolio/standard-common-repository

bangdb.com

bigobject.io

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.bangdb.com

docs.bigobject.io

Developer

Amazon

Atos Convergence Creators

Sachin Sinha, BangDB

BigObject, Inc.

Initial release

2017

2016

2012

2015

Current release

1703

BangDB 2.0, October 2021

License

Commercial or Open Source

commercial

Open Source

BSD 3

commercial

free community edition available

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

Java

C, C++

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)

Data scheme

schema-free

Schema and schema-less with LDAP views

schema-free

yes

Typing

predefined data types such as float or date

yes

optional

yes: string, long, double, int, geospatial, stream, events

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

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like DML and DDL statements

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

LDAP

Proprietary protocol
RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

All languages with LDAP bindings

C
C#
C++
Java
Python

Server-side scripts

Stored procedures

Lua

Triggers

yes

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

cell division

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

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

selectable replication factor, Knob for CAP (enterprise version only)

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 or Eventual Consistency depending on configuration

Tunable consistency, set CAP knob accordingly

none

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Read/write lock on objects (tables, trees)

Durability

Support for making data persistent

yes

with encyption-at-rest

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, run db with in-memory only mode

yes

User concepts

Access control

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

LDAP bind authentication

yes (enterprise version only)

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