DBMS > Amazon Redshift vs. AnzoGraph DB vs. Oracle Berkeley DB vs. OrigoDB

System Properties Comparison Amazon Redshift vs. AnzoGraph DB vs. Oracle Berkeley DB vs. OrigoDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Widely used in-process key-value store

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Graph DBMS
RDF store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Object oriented DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

aws.amazon.com/redshift

cambridgesemantics.com/anzograph

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

origodb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

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

origodb.com/docs

Developer

Amazon (based on PostgreSQL)

Cambridge Semantics

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Robert Friberg et al

Initial release

2012

2018

1994

2009

under the name LiveDB

Current release

2.3, January 2021

18.1.40, May 2020

License

Commercial or Open Source

commercial

free trial version available

Open Source

commercial license available

Open Source

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

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

Server operating systems

hosted

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
Windows

Data scheme

yes

Schema-free and OWL/RDFS-schema support

schema-free

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

.NET Client API
HTTP API
LINQ

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Python

.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

.Net

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

yes

Triggers

yes

only for the SQL API

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

not needed in graphs

depending on model

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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

Access rights for users and roles

Role based authorization

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