DBMS > Atos Standard Common Repository vs. Microsoft Azure Cosmos DB vs. SQream DB vs. Titan

System Properties Comparison Atos Standard Common Repository vs. Microsoft Azure Cosmos DB vs. SQream DB vs. Titan

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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.

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

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

Globally distributed, horizontally scalable, multi-model database service

a GPU-based, columnar RDBMS for big data analytics workloads

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Document store
Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	23.95
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.63
Rank	#229	Overall
	#107	Relational DBMS

Website

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

azure.microsoft.com/services/cosmos-db

sqream.com

github.com/thinkaurelius/titan

Technical documentation

learn.microsoft.com/azure/cosmos-db

docs.sqream.com

github.com/thinkaurelius/titan/wiki

Developer

Atos Convergence Creators

Microsoft

SQream Technologies

Aurelius, owned by DataStax

Initial release

2016

2014

2017

2012

Current release

1703

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache license, version 2.0

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++, CUDA, Haskell, Java, Scala

Java

Server operating systems

Linux

hosted

Linux

Linux
OS X
Unix
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

LDAP

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

.Net
JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

All languages with LDAP bindings

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

C++
Java
JavaScript (Node.js)
Python

Clojure
Java
Python

Server-side scripts

Stored procedures

JavaScript

user defined functions in Python

yes

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Implicit feature of the cloud service

horizontal and vertical partitioning

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graph

Transaction concepts

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

Atomic execution of specific operations

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

Access rights can be defined down to the item level

User authentification and security via Rexster Graph Server

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