DBMS > Amazon Redshift vs. Atos Standard Common Repository vs. DolphinDB vs. Microsoft Access vs. RDF4J

System Properties Comparison Amazon Redshift vs. Atos Standard Common Repository vs. DolphinDB vs. Microsoft Access vs. RDF4J

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

Document store
Key-value store

Time Series DBMS

Relational DBMS

RDF store

Secondary database models

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

aws.amazon.com/redshift

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

www.dolphindb.com

www.microsoft.com/en-us/microsoft-365/access

rdf4j.org

Technical documentation

docs.aws.amazon.com/redshift

docs.dolphindb.cn/en/help200/index.html

developer.microsoft.com/en-us/access

rdf4j.org/documentation

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

DolphinDB, Inc

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2012

2016

2018

1992

2004

Current release

1703

v2.00.4, January 2022

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

commercial

free community version available

commercial

Bundled with Microsoft Office

Open Source

Eclipse Distribution License (EDL), v1.0.

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++

Java

Server operating systems

hosted

Linux

Linux
Windows

Windows

Not a real database server, but making use of DLLs

Linux
OS X
Unix
Windows

Data scheme

yes

Schema and schema-less with LDAP views

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

but not compliant to any SQL standard

APIs and other access methods

JDBC
ODBC

LDAP

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

ADO.NET
DAO
ODBC
OLE DB

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

since Access 2010 using the ACE-engine

yes

Triggers

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Atomic execution of specific operations

yes

ACID

but no files for transaction logging

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

in-memory storage is supported as well

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

LDAP bind authentication

Administrators, Users, Groups

a simple user-level security was built in till version Access 2003

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