DBMS > Netezza vs. OrientDB vs. Sadas Engine vs. XTDB

System Properties Comparison Netezza vs. OrientDB vs. Sadas Engine vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Data warehouse and analytics appliance part of IBM PureSystems

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

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

Document store

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

Score	10.18
Rank	#46	Overall
	#29	Relational DBMS

Score	3.27
Rank	#97	Overall
	#16	Document stores
	#6	Graph DBMS
	#15	Key-value stores

Score	0.03
Rank	#379	Overall
	#156	Relational DBMS

Score	0.09
Rank	#351	Overall
	#47	Document stores

Website

www.ibm.com/products/netezza

orientdb.org

www.sadasengine.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

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

www.xtdb.com/docs

Developer

IBM

OrientDB LTD; CallidusCloud; SAP

SADAS s.r.l.

Juxt Ltd.

Initial release

2000

2010

2006

2019

Current release

3.2.29, March 2024

8.0

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

free trial version available

Open Source

MIT License

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

C++

Clojure

Server operating systems

Linux

included in appliance

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

AIX
Linux
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

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

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

SQL-like query language, no joins

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
OLE DB

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

JDBC
ODBC
Proprietary protocol

HTTP REST
JDBC

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

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

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

Clojure
Java

Server-side scripts

Stored procedures

yes

Java, Javascript

Triggers

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

none

yes, each node contains all data

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

relationship in graphs

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

Access rights for users and roles; record level security configurable

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

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More resources

DB-Engines blog posts

Graph DBMS increased their popularity by 500% within the last 2 years
3 March 2015, Paul Andlinger

Graph DBMSs are gaining in popularity faster than any other database category
21 January 2014, Matthias Gelbmann

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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

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

U.S. Navy Chooses Yellowbrick, Sunsets IBM Netezza
22 March 2023, Business Wire

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

provided by Google News

The 12 Best Graph Databases to Consider for 2024
22 October 2023, Solutions Review

OrientDB: A Flexible and Scalable Multi-Model NoSQL DBMS
21 January 2022, Open Source For You

Comparing Graph Databases II. Part 2: ArangoDB, OrientDB, and… | by Sam Bell
20 September 2019, Towards Data Science

ArangoDB raises $10 million for NoSQL database management
14 March 2019, VentureBeat

K2View updates DataOps platform with data fabric automation
11 May 2021, TechTarget

provided by Google News

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