DBMS > Netezza vs. Pinecone vs. Postgres-XL vs. Riak KV vs. Sadas Engine

System Properties Comparison Netezza vs. Pinecone vs. Postgres-XL vs. Riak KV vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

Data warehouse and analytics appliance part of IBM PureSystems

A managed, cloud-native vector database

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Distributed, fault tolerant key-value store

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	3.16
Rank	#95	Overall
	#2	Vector DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

www.ibm.com/products/netezza

www.pinecone.io

www.postgres-xl.org

www.sadasengine.com

Technical documentation

docs.pinecone.io/docs/overview

www.postgres-xl.org/documentation

www.tiot.jp/riak-docs/riak/kv/latest

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

IBM

Pinecone Systems, Inc

OpenSource, formerly Basho Technologies

SADAS s.r.l.

Initial release

2000

2019

2014

since 2012, originally named StormDB

2009

2006

Current release

10 R1, October 2018

3.2.0, December 2022

8.0

License

Commercial or Open Source

commercial

Open Source

Mozilla public license

Open Source

Apache version 2, commercial enterprise edition

commercial

free trial version available

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

Erlang

C++

Server operating systems

Linux

included in appliance

hosted

Linux
macOS

Linux
OS X

AIX
Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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

XML type, but no XML query functionality

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

distributed, parallel query execution

yes

APIs and other access methods

JDBC
ODBC
OLE DB

RESTful HTTP API

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

HTTP API
Native Erlang Interface

JDBC
ODBC
Proprietary protocol

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

Python

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

no "single point of failure"

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Transaction concepts

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

ACID

MVCC

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.

yes

managed by 'Learn by Usage'

User concepts

Access control

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

yes, using Riak Security

Access rights for users, groups and roles according to SQL-standard

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

IBM announces availability of the high-performance, cloud-native Netezza Performance Server as a Service on AWS
11 July 2023, ibm.com

AWS and IBM Netezza come out in support of Iceberg in table format face-off
1 August 2023, The Register

Migrating your Netezza data warehouse to Amazon Redshift | Amazon Web Services
27 May 2020, AWS Blog

IBM Brings Back a Netezza, Attacks Yellowbrick
29 June 2020, Datanami

Netezza Performance Server
12 August 2020, ibm.com

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16 January 2024, Forbes

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23 December 2011, InfoQ.com

Basho, Maker of Riak NoSQL Database, Raises $25M
13 January 2015, Data Center Knowledge

Riak NoSQL snapped up by Bet365
12 September 2017, ComputerWeekly.com

Basho distributes Riak to more customers - DCD
28 November 2013, DatacenterDynamics

provided by Google News

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