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

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

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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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Website

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

www.datomic.com

cloud.google.com/bigquery

github.com/spotify/heroic

Technical documentation

docs.datomic.com

cloud.google.com/bigquery/docs

spotify.github.io/heroic

Developer

Atos Convergence Creators

Cognitect

Google

Spotify

Initial release

2016

2012

2010

2014

Current release

1703

1.0.6735, June 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache 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

Java, Clojure

Java

Server operating systems

Linux

All OS with a Java VM

hosted

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

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

via Elasticsearch

SQL

Support of SQL

yes

APIs and other access methods

LDAP

RESTful HTTP API

RESTful HTTP/JSON API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

All languages with LDAP bindings

Clojure
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

in JavaScript

Triggers

yes

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

But extensive use of caching in the application peers

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes

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

Eventual Consistency
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

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

User concepts

Access control

LDAP bind authentication

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

Google Cloud Identity & Access Management (IAM)

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