DBMS > AnzoGraph DB vs. Datomic vs. Faircom EDGE vs. Linter

System Properties Comparison AnzoGraph DB vs. Datomic vs. Faircom EDGE vs. Linter

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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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

RDBMS for high security requirements

Primary database model

Graph DBMS
RDF store

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

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	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.datomic.com

www.faircom.com/products/faircom-edge

linter.ru

Technical documentation

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

docs.datomic.com

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

Developer

Cambridge Semantics

Cognitect

FairCom Corporation

relex.ru

Initial release

2018

2012

1979

1990

Current release

2.3, January 2021

1.0.6735, June 2023

V3, October 2020

License

Commercial or Open Source

commercial

free trial version available

commercial

limited edition free

commercial

Restricted, free version available

commercial

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

Java, Clojure

ANSI C, C++

C and C++

Server operating systems

Linux

All OS with a Java VM

Android
Linux

ARM, x86
Raspbian
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

yes

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

yes

Typing

predefined data types such as float or date

yes

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

RESTful HTTP API

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

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

C++
Java
Python

Clojure
Java

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

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Transaction Functions

yes

.Net, JavaScript, C/C++

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

But extensive use of caching in the application peers

File partitioning

Customizable business rules for partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

But extensive use of caching in the application peers

yes

Synchronous and asynchronous realtime replication based on transaction logs

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

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.

fine grained access rights according to SQL-standard

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