DBMS > Atos Standard Common Repository vs. Google BigQuery vs. Sequoiadb vs. Snowflake vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. Google BigQuery vs. Sequoiadb vs. Snowflake 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

Large scale data warehouse service with append-only tables

NewSQL database with distributed OLTP and SQL

Cloud-based data warehousing service for structured and semi-structured 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

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

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

cloud.google.com/bigquery

www.sequoiadb.com

www.snowflake.com

yaacomo.com

Technical documentation

cloud.google.com/bigquery/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

docs.snowflake.net/manuals/index.html

Developer

Atos Convergence Creators

Google

Sequoiadb Ltd.

Snowflake Computing Inc.

Q2WEB GmbH

Initial release

2016

2010

2013

2014

2009

Current release

1703

License

Commercial or Open Source

commercial

Open Source

Server: AGPL; Client: Apache V2

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

C++

Server operating systems

Linux

hosted

Linux

hosted

Android
Linux
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

optional

yes

oid, date, timestamp, binary, regex

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

SQL-like query language

yes

APIs and other access methods

LDAP

RESTful HTTP/JSON API

proprietary protocol using JSON

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C++
Java
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

user defined functions

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

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

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

Since BigQuery is designed for querying data

Document is locked during a transaction

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

User concepts

Access control

LDAP bind authentication

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

Google Cloud Identity & Access Management (IAM)

simple password-based access control

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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