DBMS > Atos Standard Common Repository vs. Hazelcast vs. JanusGraph vs. Microsoft SQL Server vs. Snowflake

System Properties Comparison Atos Standard Common Repository vs. Hazelcast vs. JanusGraph vs. Microsoft SQL Server vs. Snowflake

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Microsofts flagship relational DBMS

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

Primary database model

Document store
Key-value store

Key-value store

Graph DBMS

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Graph DBMS
Spatial DBMS

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

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	1.85
Rank	#134	Overall
	#12	Graph DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

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

hazelcast.com

janusgraph.org

www.microsoft.com/en-us/sql-server

www.snowflake.com

Technical documentation

hazelcast.org/imdg/docs

docs.janusgraph.org

learn.microsoft.com/en-US/sql/sql-server

docs.snowflake.net/manuals/index.html

Developer

Atos Convergence Creators

Hazelcast

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Snowflake Computing Inc.

Initial release

2016

2008

2017

1989

2014

Current release

1703

5.3.6, November 2023

1.0.0, October 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Server operating systems

Linux

All OS with a Java VM

Linux
OS X
Unix
Windows

Linux
Windows

hosted

Data scheme

Schema and schema-less with LDAP views

schema-free

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

LDAP

JCache
JPA
Memcached protocol
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

CLI Client
JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

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

Clojure
Java
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

user defined functions

Triggers

yes

Events

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

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

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

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

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

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

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