DBMS > Amazon Redshift vs. Graph Engine vs. Oracle Rdb

System Properties Comparison Amazon Redshift vs. Graph Engine vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

aws.amazon.com/redshift

www.graphengine.io

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

Technical documentation

docs.aws.amazon.com/redshift

www.graphengine.io/docs/manual

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Amazon (based on PostgreSQL)

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2012

2010

1984

Current release

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

MIT License

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

.NET and C

Server operating systems

hosted

.NET

HP Open VMS

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
F#
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

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