DBMS > Atos Standard Common Repository vs. Google BigQuery vs. H2 vs. HyperSQL vs. Kdb

System Properties Comparison Atos Standard Common Repository vs. Google BigQuery vs. H2 vs. HyperSQL vs. Kdb

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

High performance Time Series DBMS

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS
Vector DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Website

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

cloud.google.com/bigquery

www.h2database.com

hsqldb.org

kx.com

Technical documentation

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

hsqldb.org/web/hsqlDocsFrame.html

code.kx.com

Developer

Atos Convergence Creators

Google

Thomas Mueller

Kx Systems, a division of First Derivatives plc

Initial release

2016

2010

2005

2001

2000

kdb was released 2000, kdb+ in 2003

Current release

1703

2.2.220, July 2023

2.7.2, June 2023

3.6, May 2018

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

based on BSD license

commercial

free 32-bit version

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

Server operating systems

Linux

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
OS X
Solaris
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

table attribute 'grouped'

SQL

Support of SQL

yes

SQL-like query language (q)

APIs and other access methods

LDAP

RESTful HTTP/JSON API

JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

Supported programming languages

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

All languages supporting JDBC/ODBC
Java

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

Java, SQL

user defined functions

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

With clustering: 2 database servers on different computers operate on identical copies of a database

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

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

yes, multi-version concurrency control (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

rights management via user accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

upon request
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.