DBMS > AnzoGraph DB vs. JanusGraph vs. NCache vs. Netezza vs. Snowflake

System Properties Comparison AnzoGraph DB vs. JanusGraph vs. NCache vs. Netezza vs. Snowflake

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 Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Open-Source and Enterprise in-memory Key-Value Store

Data warehouse and analytics appliance part of IBM PureSystems

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

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

cambridgesemantics.com/anzograph

janusgraph.org

www.alachisoft.com/ncache

www.ibm.com/products/netezza

www.snowflake.com

Technical documentation

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

docs.janusgraph.org

www.alachisoft.com/resources/docs

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

Linux Foundation; originally developed as Titan by Aurelius

Alachisoft

IBM

Snowflake Computing Inc.

Initial release

2018

2017

2005

2000

2014

Current release

2.3, January 2021

0.6.3, February 2023

5.3.3, April 2024

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache 2.0

Open Source

Enterprise Edition available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C#, .NET, .NET Core, Java

Server operating systems

Linux

Linux
OS X
Unix
Windows

Linux
Windows

Linux

included in appliance

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

yes

APIs and other access methods

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

IDistributedCache
JCache
LINQ
Proprietary native API

JDBC
ODBC
OLE DB

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

Clojure
Java
Python

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

C
C++
Fortran
Java
Lua
Perl
Python
R

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

support for stored procedures with SQL-Server CLR

yes

user defined functions

Triggers

yes

Notifications

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

yes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

yes, with selectable consistency level

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

via Faunus, a graph analytics engine

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Eventual Consistency
Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

optimistic locking and pessimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

User authentification and security via Rexster Graph Server

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

Users with fine-grained authorization concept

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

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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