DBMS > Amazon Redshift vs. BigObject vs. Blueflood vs. Oracle Berkeley DB vs. RDFox

System Properties Comparison Amazon Redshift vs. BigObject vs. Blueflood vs. Oracle Berkeley DB vs. RDFox

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for real-time computations and queries

Scalable TimeSeries DBMS based on Cassandra

Widely used in-process key-value store

High performance knowledge graph and semantic reasoning engine

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Graph DBMS
RDF store

Secondary database models

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

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

Website

aws.amazon.com/redshift

bigobject.io

blueflood.io

www.oracle.com/database/technologies/related/berkeleydb.html

www.oxfordsemantic.tech

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

github.com/rax-maas/blueflood/wiki

docs.oracle.com/cd/E17076_05/html/index.html

docs.oxfordsemantic.tech

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Rackspace

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Oxford Semantic Technologies

Initial release

2012

2015

2013

1994

2017

Current release

18.1.40, May 2020

6.0, Septermber 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

Open Source

commercial license available

commercial

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, Java, C++ (depending on the Berkeley DB edition)

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

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
macOS
Windows

Data scheme

yes

predefined scheme

schema-free

yes

RDF schemas

Typing

predefined data types such as float or date

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

only with the Berkeley DB XML edition

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

HTTP REST

RESTful HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
Java

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor

based on Cassandra

Source-replica replication

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

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

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

Roles, resources, and access types

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