DBMS > Datomic vs. Netezza vs. VoltDB

System Properties Comparison Datomic vs. Netezza vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

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

Score	1.55
Rank	#144	Overall
	#67	Relational DBMS

Score	7.56
Rank	#48	Overall
	#31	Relational DBMS

Score	1.38
Rank	#156	Overall
	#72	Relational DBMS

Website

www.datomic.com

www.ibm.com/products/netezza

www.voltdb.com

Technical documentation

docs.datomic.com

docs.voltdb.com

Developer

Cognitect

IBM

VoltDB Inc.

Initial release

2012

2000

2010

Current release

1.0.7180, July 2024

11.3, April 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, Clojure

Java, C++

Server operating systems

All OS with a Java VM

Linux

included in appliance

Linux
OS X

for development

Data scheme

yes

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

yes

SQL

Support of SQL

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
OLE DB

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Clojure
Java

C
C++
Fortran
Java
Lua
Perl
Python
R

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Java

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

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

yes

Snapshots and command logging

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

Users with fine-grained authorization concept

Users and roles with access to stored procedures

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