DBMS > Amazon Redshift vs. Bangdb vs. BigObject vs. Blueflood vs. RDFox

System Properties Comparison Amazon Redshift vs. Bangdb vs. BigObject vs. Blueflood vs. RDFox

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

Relational DBMS

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	16.88
Rank	#35	Overall
	#22	Relational DBMS

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

aws.amazon.com/redshift

bangdb.com

bigobject.io

blueflood.io

www.oxfordsemantic.tech

Technical documentation

docs.aws.amazon.com/redshift

docs.bangdb.com

docs.bigobject.io

github.com/rax-maas/blueflood/wiki

docs.oxfordsemantic.tech

Developer

Amazon (based on PostgreSQL)

Sachin Sinha, BangDB

BigObject, Inc.

Rackspace

Oxford Semantic Technologies

Initial release

2012

2015

2013

2017

Current release

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

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java

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)

Linux
OS X

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

predefined scheme

yes

RDF schemas

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

restricted

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL like support with command line tool

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Proprietary protocol
RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

HTTP REST

RESTful HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
Python

C
Java

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

Triggers

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

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

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

Transaction concepts

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

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

fine grained access rights according to SQL-standard

yes (enterprise version only)

Roles, resources, and access types

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