DBMS > Atos Standard Common Repository vs. Hazelcast vs. Microsoft Azure Cosmos DB vs. Warp 10

System Properties Comparison Atos Standard Common Repository vs. Hazelcast vs. Microsoft Azure Cosmos DB vs. Warp 10

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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

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

A widely adopted in-memory data grid

Globally distributed, horizontally scalable, multi-model database service

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store
Key-value store

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

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

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

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

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

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

hazelcast.com

azure.microsoft.com/services/cosmos-db

www.warp10.io

Technical documentation

hazelcast.org/imdg/docs

learn.microsoft.com/azure/cosmos-db

www.warp10.io/content/02_Getting_started

Developer

Atos Convergence Creators

Hazelcast

Microsoft

SenX

Initial release

2016

2008

2014

2015

Current release

1703

5.3.6, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache License 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

Server operating systems

Linux

All OS with a Java VM

hosted

Linux
OS X
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

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

the object must implement a serialization strategy

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

APIs and other access methods

LDAP

JCache
JPA
Memcached protocol
RESTful HTTP API

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

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages with LDAP bindings

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

JavaScript

yes

WarpScript

Triggers

yes

Events

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Implicit feature of the cloud service

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Implicit feature of the cloud service

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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 or Eventual Consistency selectable by user

Raft Consensus Algorithm

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

one or two-phase-commit; repeatable reads; read commited

Multi-item ACID transactions with snapshot isolation within a partition

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

Role-based access control

Access rights can be defined down to the item level

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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