DBMS > AnzoGraph DB vs. Faircom EDGE vs. Netezza vs. Snowflake vs. SpatiaLite

System Properties Comparison AnzoGraph DB vs. Faircom EDGE vs. Netezza vs. Snowflake vs. SpatiaLite

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

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Data warehouse and analytics appliance part of IBM PureSystems

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

Spatial extension of SQLite

Primary database model

Graph DBMS
RDF store

Key-value store
Relational DBMS

Relational DBMS

Spatial DBMS

Secondary database models

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	0.03
Rank	#380	Overall
	#59	Key-value stores
	#157	Relational DBMS

Score	10.18
Rank	#46	Overall
	#29	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	1.72
Rank	#149	Overall
	#3	Spatial DBMS

Website

cambridgesemantics.com/anzograph

www.faircom.com/products/faircom-edge

www.ibm.com/products/netezza

www.snowflake.com

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

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

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

docs.snowflake.net/manuals/index.html

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Cambridge Semantics

FairCom Corporation

IBM

Snowflake Computing Inc.

Alessandro Furieri

Initial release

2018

1979

2000

2014

2008

Current release

2.3, January 2021

V3, October 2020

5.0.0, August 2020

License

Commercial or Open Source

commercial

free trial version available

commercial

Restricted, free version available

commercial

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

C++

Server operating systems

Linux

Android
Linux

ARM, x86
Raspbian
Windows

Linux

included in appliance

hosted

server-less

Data scheme

Schema-free and OWL/RDFS-schema support

Flexible Schema (defined schema, partial schema, schema free)

yes

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

yes

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

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.

yes

ANSI SQL queries

yes

APIs and other access methods

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

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC
OLE DB

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

C
C++
Fortran
Java
Lua
Perl
Python
R

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

.Net, JavaScript, C/C++

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

File partitioning

Customizable business rules for partitioning

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

Synchronous and asynchronous realtime replication based on transaction logs

Source-replica replication

yes

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
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

when using SQL

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Users with fine-grained authorization concept

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

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