DBMS > Atos Standard Common Repository vs. CouchDB vs. Google BigQuery vs. Microsoft Azure Synapse Analytics vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. CouchDB vs. Google BigQuery vs. Microsoft Azure Synapse Analytics vs. Yaacomo

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 native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Large scale data warehouse service with append-only tables

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

Primary database model

Document store
Key-value store

Document store

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Website

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

couchdb.apache.org

cloud.google.com/bigquery

azure.microsoft.com/services/synapse-analytics

yaacomo.com

Technical documentation

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

docs.microsoft.com/azure/synapse-analytics

Developer

Atos Convergence Creators

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Microsoft

Q2WEB GmbH

Initial release

2016

2005

2010

2016

2009

Current release

1703

3.3.3, December 2023

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

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

Erlang

C++

Server operating systems

Linux

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Android
Linux
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

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

APIs and other access methods

LDAP

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
PHP

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

in JavaScript

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

improved architecture with release 2.0

none

Sharding, horizontal partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

Atomic execution of specific operations

atomic operations within a single document possible

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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

Access rights for users can be defined per database

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

Google Cloud Identity & Access Management (IAM)

yes

fine grained access rights according to SQL-standard

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