DBMS > Atos Standard Common Repository vs. dBASE vs. Google BigQuery vs. Memcached vs. Splice Machine

System Properties Comparison Atos Standard Common Repository vs. dBASE vs. Google BigQuery vs. Memcached vs. Splice Machine

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

dBase was one of the first databases with a development environment on PC's. Its latest version dBase V is still sold as dBase classic, which needs a DOS Emulation. The up-to-date product is dBase plus.

Large scale data warehouse service with append-only tables

In-memory key-value store, originally intended for caching

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store

Relational DBMS

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

Score	9.70
Rank	#44	Overall
	#28	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

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

www.dbase.com

cloud.google.com/bigquery

www.memcached.org

splicemachine.com

Technical documentation

www.dbase.com/support/knowledgebase

cloud.google.com/bigquery/docs

github.com/memcached/memcached/wiki

splicemachine.com/how-it-works

Developer

Atos Convergence Creators

Asthon Tate

Google

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Splice Machine

Initial release

2016

1979

2010

2003

2014

Current release

1703

dBASE 2019, 2019

1.6.27, May 2024

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

BSD license

Open Source

AGPL 3.0, 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

Server operating systems

Linux

DOS

dBase Classic
Windows

dBase Pro

hosted

FreeBSD
Linux
OS X
Unix
Windows

Linux
OS X
Solaris
Windows

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

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

SQL

Support of SQL

yes

APIs and other access methods

LDAP

none

The IDE can access other DBMS or ODBC-sources.

RESTful HTTP/JSON API

Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages with LDAP bindings

dBase proprietary IDE

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

The IDE can access stored procedures in other database systems.

user defined functions

in JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Repcached, a Memcached patch, provides this functionallity

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark 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

not for dBase internal data, but IDE does support transactions when accessing external DBMS

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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 rights for users and roles

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

Google Cloud Identity & Access Management (IAM)

yes

using SASL (Simple Authentication and Security Layer) protocol

Access rights for users, groups and roles according to SQL-standard

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