DBMS > Atos Standard Common Repository vs. DataFS vs. Microsoft Azure Cosmos DB vs. Snowflake vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. DataFS vs. Microsoft Azure Cosmos DB vs. Snowflake vs. Yaacomo

Editorial information provided by DB-Engines

Name

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

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Globally distributed, horizontally scalable, multi-model database service

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Key-value store

Object oriented DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Graph DBMS

Spatial DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

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

newdatabase.com

azure.microsoft.com/services/cosmos-db

www.snowflake.com

yaacomo.com

Technical documentation

dev.mobiland.com/Overview.xsp

learn.microsoft.com/azure/cosmos-db

docs.snowflake.net/manuals/index.html

Developer

Atos Convergence Creators

Mobiland AG

Microsoft

Snowflake Computing Inc.

Q2WEB GmbH

Initial release

2016

2018

2014

2009

Current release

1703

1.1.263, October 2022

License

Commercial or Open Source

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

Server operating systems

Linux

Windows

hosted

Android
Linux
Windows

Data scheme

Schema and schema-less with LDAP views

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

yes

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

yes

Typing

predefined data types such as float or date

optional

yes

JSON 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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

LDAP

.NET Client API
Proprietary client DLL
WinRT client

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

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

.Net
C
C#
C++
VB.Net

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

JavaScript

user defined functions

Triggers

yes

no, except callback-events from server when changes happened

JavaScript

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Proprietary Sharding system

Sharding

Implicit feature of the cloud service

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

User concepts

Access control

LDAP bind authentication

Windows-Profile

Access rights can be defined down to the item level

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

fine grained access rights according to SQL-standard

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