DBMS > Citus vs. Netezza vs. OushuDB vs. Snowflake

System Properties Comparison Citus vs. Netezza vs. OushuDB vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Data warehouse and analytics appliance part of IBM PureSystems

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Secondary database models

Document store

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

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	0.10
Rank	#355	Overall
	#154	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.citusdata.com

www.ibm.com/products/netezza

www.oushu.com/product/oushuDB

www.snowflake.com

Technical documentation

docs.citusdata.com

www.oushu.com/documentation

docs.snowflake.net/manuals/index.html

Developer

IBM

Oushu

Snowflake Computing Inc.

Initial release

2010

2000

2014

Current release

8.1, December 2018

4.0.1, August 2020

License

Commercial or Open Source

Open Source

AGPL, commercial license also 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

Server operating systems

Linux

included in appliance

Linux

hosted

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

Full-featured ANSI SQL support

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC
OLE DB

JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C++
Fortran
Java
Lua
Perl
Python
R

C
C++

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

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

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

Kerberos, SSL and role based access

Users with fine-grained authorization concept, user roles and pluggable authentication

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