DBMS > AnzoGraph DB vs. LeanXcale vs. Netezza vs. Sadas Engine

System Properties Comparison AnzoGraph DB vs. LeanXcale vs. Netezza vs. Sadas Engine

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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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Data warehouse and analytics appliance part of IBM PureSystems

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

Primary database model

Graph DBMS
RDF store

Key-value store
Relational DBMS

Relational DBMS

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

Score	0.27
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.35
Rank	#283	Overall
	#41	Key-value stores
	#128	Relational DBMS

Score	10.18
Rank	#46	Overall
	#29	Relational DBMS

Score	0.03
Rank	#379	Overall
	#156	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.leanxcale.com

www.ibm.com/products/netezza

www.sadasengine.com

Technical documentation

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

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

Developer

Cambridge Semantics

LeanXcale

IBM

SADAS s.r.l.

Initial release

2018

2015

2000

2006

Current release

2.3, January 2021

8.0

License

Commercial or Open Source

commercial

free trial version available

commercial

free trial version available

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

C++

Server operating systems

Linux

included in appliance

AIX
Linux
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

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

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

yes

through Apache Derby

yes

APIs and other access methods

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

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

JDBC
ODBC
OLE DB

JDBC
ODBC
Proprietary protocol

Supported programming languages

C++
Java
Python

C
Java
Scala

C
C++
Fortran
Java
Lua
Perl
Python
R

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

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

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

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

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

Access rights for users and roles

Users with fine-grained authorization concept

Access rights for users, groups and roles 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

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