DBMS > 4D vs. AnzoGraph DB vs. Atos Standard Common Repository vs. Microsoft Azure Cosmos DB vs. Speedb

System Properties Comparison 4D vs. AnzoGraph DB vs. Atos Standard Common Repository vs. Microsoft Azure Cosmos DB vs. Speedb

Editorial information provided by DB-Engines

Name

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

Description

Application development environment with integrated database management system

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

Globally distributed, horizontally scalable, multi-model database service

An embeddable, high performance key-value store optimized for write-intensive workloads, which can be used as a drop-in replacement for RocksDB

Primary database model

Relational DBMS

Graph DBMS
RDF store

Document store
Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Secondary database models

Spatial DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.21
Rank	#315	Overall
	#45	Key-value stores

Website

www.4d.com

cambridgesemantics.com/anzograph

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

azure.microsoft.com/services/cosmos-db

www.speedb.io

Technical documentation

developer.4d.com

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

learn.microsoft.com/azure/cosmos-db

Developer

4D, Inc

Cambridge Semantics

Atos Convergence Creators

Microsoft

Speedb

Initial release

1984

2018

2016

2014

2020

Current release

v20, April 2023

2.3, January 2021

1703

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

Apache Version 2.0; commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

OS X
Windows

Linux

hosted

Linux
Windows

Data scheme

yes

Schema-free and OWL/RDFS-schema support

Schema and schema-less with LDAP views

schema-free

Typing

predefined data types such as float or date

yes

optional

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

yes

close to SQL 92

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

SQL-like query language

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

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

LDAP

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

4D proprietary IDE
PHP

C++
Java
Python

All languages with LDAP bindings

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Automatic sharding

Sharding

cell division

Sharding

Implicit feature of the cloud service

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication in MPP-Cluster

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

not needed in graphs

Transaction concepts

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

ACID

Atomic execution of specific operations

Multi-item ACID transactions with snapshot isolation within a partition

yes

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

User concepts

Access control

Users and groups

Access rights for users and roles

LDAP bind authentication

Access rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Licensing and pricing models

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