DBMS > AnzoGraph DB vs. Atos Standard Common Repository vs. MarkLogic vs. Snowflake

System Properties Comparison AnzoGraph DB vs. Atos Standard Common Repository vs. MarkLogic vs. Snowflake

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed 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

Operational and transactional Enterprise NoSQL database

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

Primary database model

Graph DBMS
RDF store

Document store
Key-value store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational 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	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

cambridgesemantics.com/anzograph

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

www.marklogic.com

www.snowflake.com

Technical documentation

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

docs.marklogic.com

docs.snowflake.net/manuals/index.html

Developer

Cambridge Semantics

Atos Convergence Creators

MarkLogic Corp.

Snowflake Computing Inc.

Initial release

2018

2016

2001

2014

Current release

2.3, January 2021

1703

11.0, December 2022

License

Commercial or Open Source

commercial

free trial version available

commercial

restricted free version is available

commercial

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

Linux

Linux
OS X
Windows

hosted

Data scheme

Schema-free and OWL/RDFS-schema support

Schema and schema-less with LDAP views

schema-free

Schema can be enforced

yes

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

Typing

predefined data types such as float or date

optional

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

SQL92

yes

APIs and other access methods

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

LDAP

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Python

All languages with LDAP bindings

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

via XQuery or JavaScript

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

cell division

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

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

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

can act as a resource manager in an XA/JTA transaction

ACID

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

yes, with Range Indexes

User concepts

Access control

Access rights for users and roles

LDAP bind authentication

Role-based access control at the document and subdocument levels

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

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