DBMS > Amazon Redshift vs. atoti vs. Datomic vs. Netezza vs. OrientDB

System Properties Comparison Amazon Redshift vs. atoti vs. Datomic vs. Netezza vs. OrientDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Data warehouse and analytics appliance part of IBM PureSystems

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Object oriented DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Website

aws.amazon.com/redshift

atoti.io

www.datomic.com

www.ibm.com/products/netezza

orientdb.org

Technical documentation

docs.aws.amazon.com/redshift

docs.atoti.io

docs.datomic.com

www.orientdb.com/docs/last/index.html

Developer

Amazon (based on PostgreSQL)

ActiveViam

Cognitect

IBM

OrientDB LTD; CallidusCloud; SAP

Initial release

2012

2000

2010

Current release

1.0.6735, June 2023

3.2.29, March 2024

License

Commercial or Open Source

commercial

free versions available

commercial

limited edition free

commercial

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, Clojure

Java

Server operating systems

hosted

All OS with a Java VM

Linux

included in appliance

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

Multidimensional Expressions (MDX)

yes

SQL-like query language, no joins

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC
ODBC
OLE DB

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

C
C++
Fortran
Java
Lua
Perl
Python
R

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

Python

yes

Transaction Functions

yes

Java, Javascript

Triggers

By using transaction functions

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

could be achieved with distributed queries

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

relationship in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

Access rights for users and roles; record level security configurable

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