DBMS > Atos Standard Common Repository vs. EXASOL vs. FeatureBase vs. Google BigQuery vs. Oracle Berkeley DB

System Properties Comparison Atos Standard Common Repository vs. EXASOL vs. FeatureBase vs. Google BigQuery vs. Oracle Berkeley DB

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

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Large scale data warehouse service with append-only tables

Widely used in-process key-value store

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

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

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	0.31
Rank	#292	Overall
	#135	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

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

www.exasol.com

www.featurebase.com

cloud.google.com/bigquery

www.oracle.com/database/technologies/related/berkeleydb.html

Technical documentation

www.exasol.com/resources

docs.featurebase.com

cloud.google.com/bigquery/docs

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Atos Convergence Creators

Exasol

Molecula and Pilosa Open Source Contributors

Google

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2016

2000

2017

2010

1994

Current release

1703

2022, May 2022

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

commercial license available

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, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

Linux

Linux
macOS

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL queries

yes

SQL interfaced based on SQLite is available

APIs and other access methods

LDAP

.Net
JDBC
ODBC
WebSocket

gRPC
JDBC
Kafka Connector
ODBC

RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

Java
Lua
Python
R

Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

ACID

yes

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

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, groups and roles according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

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