DBMS > Atos Standard Common Repository vs. Hazelcast vs. JaguarDB vs. Spark SQL vs. Transbase

System Properties Comparison Atos Standard Common Repository vs. Hazelcast vs. JaguarDB vs. Spark SQL vs. Transbase

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

Performant, highly scalable DBMS for AI and IoT applications

Spark SQL is a component on top of 'Spark Core' for structured data processing

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Document store
Key-value store

Key-value store

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

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

hazelcast.com

www.jaguardb.com

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

Technical documentation

hazelcast.org/imdg/docs

www.jaguardb.com/support.html

spark.apache.org/docs/latest/sql-programming-guide.html

www.transaction.de/en/products/transbase/features.html

Developer

Atos Convergence Creators

Hazelcast

DataJaguar, Inc.

Apache Software Foundation

Transaction Software GmbH

Initial release

2016

2008

2015

2014

1987

Current release

1703

5.3.6, November 2023

3.3 July 2023

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL V3.0

Open Source

Apache 2.0

commercial

free development license

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

the server part. Clients available in other languages

Scala

C and C++

Server operating systems

Linux

All OS with a Java VM

Linux

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like DML and DDL statements

yes

APIs and other access methods

LDAP

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages with LDAP bindings

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Multi-source replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Foreign keys

Referential integrity

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

yes

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

rights management via user accounts

fine grained access rights according to SQL-standard

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