DBMS > Atos Standard Common Repository vs. Bangdb vs. BigObject vs. Blueflood vs. RDFox

System Properties Comparison Atos Standard Common Repository vs. Bangdb vs. BigObject vs. Blueflood vs. RDFox

Editorial information provided by DB-Engines

Name

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

Description

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

Scalable TimeSeries DBMS based on Cassandra

High performance knowledge graph and semantic reasoning engine

Primary database model

Document store
Key-value store

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.29
Rank	#300	Overall
	#24	Graph DBMS
	#13	RDF stores

Website

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

bangdb.com

bigobject.io

blueflood.io

www.oxfordsemantic.tech

Technical documentation

docs.bangdb.com

docs.bigobject.io

github.com/rax-maas/blueflood/wiki

docs.oxfordsemantic.tech

Developer

Atos Convergence Creators

Sachin Sinha, BangDB

BigObject, Inc.

Rackspace

Oxford Semantic Technologies

Initial release

2016

2012

2015

2013

2017

Current release

1703

BangDB 2.0, October 2021

6.0, Septermber 2022

License

Commercial or Open Source

commercial

Open Source

BSD 3

commercial

free community edition available

Open Source

Apache 2.0

commercial

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

Java

C, C++

Java

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X

Linux
macOS
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

predefined scheme

yes

RDF schemas

Typing

predefined data types such as float or date

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

LDAP

Proprietary protocol
RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

HTTP REST

RESTful HTTP API
SPARQL 1.1

Supported programming languages

All languages with LDAP bindings

C
C#
C++
Java
Python

C
Java

Server-side scripts

Stored procedures

Lua

Triggers

yes

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

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

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

selectable replication factor

based on Cassandra

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Tunable consistency, set CAP knob accordingly

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

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)

yes

Durability

Support for making data persistent

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

LDAP bind authentication

yes (enterprise version only)

Roles, resources, and access types

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