DBMS > AnzoGraph DB vs. mSQL vs. Netezza vs. Postgres-XL vs. SwayDB

System Properties Comparison AnzoGraph DB vs. mSQL vs. Netezza vs. Postgres-XL vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

mSQL (Mini SQL) is a simple and lightweight RDBMS

Data warehouse and analytics appliance part of IBM PureSystems

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

cambridgesemantics.com/anzograph

hughestech.com.au/products/msql

www.ibm.com/products/netezza

www.postgres-xl.org

swaydb.simer.au

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

www.postgres-xl.org/documentation

Developer

Cambridge Semantics

Hughes Technologies

IBM

Simer Plaha

Initial release

2018

1994

2000

2014

since 2012, originally named StormDB

2018

Current release

2.3, January 2021

4.4, October 2021

10 R1, October 2018

License

Commercial or Open Source

commercial

free trial version available

commercial

free licenses can be provided

commercial

Open Source

Mozilla public license

Open Source

GNU Affero GPL V3.0

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

Scala

Server operating systems

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux

included in appliance

Linux
macOS

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

distributed, parallel query execution

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

JDBC
ODBC

JDBC
ODBC
OLE DB

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

Supported programming languages

C++
Java
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

C
C++
Fortran
Java
Lua
Perl
Python
R

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

none

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

MVCC

Atomic execution of operations

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

User concepts

Access control

Access rights for users and roles

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

AnzoGraph review: A graph database for deep analytics
15 April 2019, InfoWorld

Cambridge Semantics Fits AnzoGraph DB with More Speed, Free Access
23 January 2020, Solutions Review

AnzoGraph: A W3C Standards-Based Graph Database | by Jo Stichbury
8 February 2019, Towards Data Science

Back to the future: Does graph database success hang on query language?
5 March 2018, ZDNet

Is The Enterprise Knowledge Graph Finally Going To Make All Data Usable?
19 September 2018, Forbes

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29 November 2018, diggers.news

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

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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

provided by Google News

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