DBMS > Atos Standard Common Repository vs. HEAVY.AI vs. Realm vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. HEAVY.AI vs. Realm vs. Yaacomo

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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.

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

Primary database model

Document store
Key-value store

Relational DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	7.18
Rank	#52	Overall
	#8	Document stores

Website

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

github.com/heavyai/heavydb
www.heavy.ai

realm.io

yaacomo.com

Technical documentation

docs.heavy.ai

realm.io/docs

Developer

Atos Convergence Creators

HEAVY.AI, Inc.

Realm, acquired by MongoDB in May 2019

Q2WEB GmbH

Initial release

2016

2014

2009

Current release

1703

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

commercial

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++ and CUDA

Server operating systems

Linux

Android
Backend: server-less
iOS
Windows

Android
Linux
Windows

Data scheme

Schema and schema-less with LDAP views

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

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

LDAP

JDBC
ODBC
Thrift
Vega

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

All languages supporting JDBC/ODBC/Thrift
Python

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Round robin

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

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

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

In-Memory realm

yes

User concepts

Access control

LDAP bind authentication

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

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