DBMS > AnzoGraph DB vs. Atos Standard Common Repository vs. Faircom EDGE vs. Snowflake vs. Trafodion

System Properties Comparison AnzoGraph DB vs. Atos Standard Common Repository vs. Faircom EDGE vs. Snowflake vs. Trafodion

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

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

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Graph DBMS
RDF store

Document store
Key-value store

Key-value store
Relational DBMS

Relational DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

cambridgesemantics.com/anzograph

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.faircom.com/products/faircom-edge

www.snowflake.com

trafodion.apache.org

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

trafodion.apache.org/documentation.html

Developer

Cambridge Semantics

Atos Convergence Creators

FairCom Corporation

Snowflake Computing Inc.

Apache Software Foundation, originally developed by HP

Initial release

2018

2016

1979

2014

Current release

2.3, January 2021

1703

V3, October 2020

2.3.0, February 2019

License

Commercial or Open Source

commercial

free trial version available

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

ANSI C, C++

C++, Java

Server operating systems

Linux

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux

Data scheme

Schema-free and OWL/RDFS-schema support

Schema and schema-less with LDAP views

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

optional

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

LDAP

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

CLI Client
JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

C++
Java
Python

All languages with LDAP bindings

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

JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

.Net, JavaScript, C/C++

user defined functions

Java Stored Procedures

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

cell division

File partitioning

Customizable business rules for partitioning

yes

Sharding

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

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency or Eventual Consistency depending on configuration

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

Atomic execution of specific operations

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

LDAP bind authentication

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

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

fine grained access rights according to SQL-standard

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