DBMS > Atos Standard Common Repository vs. Datomic vs. Google BigQuery vs. Realm

System Properties Comparison Atos Standard Common Repository vs. Datomic vs. Google BigQuery vs. Realm

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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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Large scale data warehouse service with append-only tables

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

Primary database model

Document store
Key-value store

Relational DBMS

Document store

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Website

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

www.datomic.com

cloud.google.com/bigquery

realm.io

Technical documentation

docs.datomic.com

cloud.google.com/bigquery/docs

realm.io/docs

Developer

Atos Convergence Creators

Cognitect

Google

Realm, acquired by MongoDB in May 2019

Initial release

2016

2012

2010

2014

Current release

1703

1.0.7075, December 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

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

Java, Clojure

Server operating systems

Linux

All OS with a Java VM

hosted

Android
Backend: server-less
iOS
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

RESTful HTTP API

RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

Clojure
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

in JavaScript

runs within the applications so server-side scripts are unnecessary

Triggers

yes

By using transaction functions

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

But extensive use of caching in the application peers

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

none

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

Transaction concepts

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

Atomic execution of specific operations

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

In-Memory realm

User concepts

Access control

LDAP bind authentication

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

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