DBMS > Atos Standard Common Repository vs. Google BigQuery vs. HBase vs. Kingbase vs. Newts

System Properties Comparison Atos Standard Common Repository vs. Google BigQuery vs. HBase vs. Kingbase vs. Newts

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

Time Series DBMS based on Cassandra

Primary database model

Document store
Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.45
Rank	#262	Overall
	#123	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

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

cloud.google.com/bigquery

hbase.apache.org

www.kingbase.com.cn

opennms.github.io/newts

Technical documentation

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

github.com/OpenNMS/newts/wiki

Developer

Atos Convergence Creators

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

BeiJing KINGBASE Information technologies inc.

OpenNMS Group

Initial release

2016

2010

2008

1999

2014

Current release

1703

2.3.4, January 2021

V8.0, August 2021

License

Commercial or Open Source

commercial

Open Source

Apache version 2

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

C and Java

Java

Server operating systems

Linux

hosted

Linux
Unix
Windows

using Cygwin

Linux
Windows

Linux
OS X
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

options to bring your own types, AVRO

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

Standard with numerous extensions

APIs and other access methods

LDAP

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

HTTP REST
Java API

Supported programming languages

All languages with LDAP bindings

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Coprocessors in Java

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

selectable replication factor

based on Cassandra

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

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Single row ACID (across millions of columns)

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)

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.