DBMS > Atos Standard Common Repository vs. Google BigQuery vs. Ingres vs. SQream DB

System Properties Comparison Atos Standard Common Repository vs. Google BigQuery vs. Ingres vs. SQream DB

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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

Large scale data warehouse service with append-only tables

Well established RDBMS

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Document store
Key-value store

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

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

cloud.google.com/bigquery

www.actian.com/databases/ingres

sqream.com

Technical documentation

cloud.google.com/bigquery/docs

docs.actian.com/ingres

docs.sqream.com

Developer

Atos Convergence Creators

Google

Actian Corporation

SQream Technologies

Initial release

2016

2010

1974

originally developed at University Berkely in early 1970s

2017

Current release

1703

11.2, May 2022

2022.1.6, December 2022

License

Commercial or Open Source

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++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

optional

yes

yes, ANSI Standard SQL Types

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

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

LDAP

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

.Net
JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Ingres Replicator

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

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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)

fine grained access rights according to SQL-standard

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