DBMS > Amazon Redshift vs. Atos Standard Common Repository vs. Microsoft Access vs. ObjectBox vs. Solr

System Properties Comparison Amazon Redshift vs. Atos Standard Common Repository vs. Microsoft Access vs. ObjectBox vs. Solr

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

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.)

Extremely fast embedded database for small devices, IoT and Mobile

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

Object oriented DBMS

Search engine

Secondary database models

Time Series DBMS

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

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

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

objectbox.io

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

developer.microsoft.com/en-us/access

docs.objectbox.io

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

Microsoft

ObjectBox Limited

Apache Software Foundation

Initial release

2012

2016

1992

2017

2006

Current release

1703

1902 (16.0.11328.20222), March 2019

9.5.0, February 2024

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

Open Source

Apache License 2.0

Open Source

Apache Version 2

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++

C and C++

Java

Server operating systems

hosted

Linux

Windows

Not a real database server, but making use of DLLs

Android
iOS
Linux
macOS
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

Schema and schema-less with LDAP views

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

optional

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

but not compliant to any SQL standard

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

LDAP

ADO.NET
DAO
ODBC
OLE DB

Proprietary native API

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

since Access 2010 using the ACE-engine

Java plugins

Triggers

yes

since Access 2010 using the ACE-engine

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

online/offline synchronization between client and server

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual 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

ACID

but no files for transaction logging

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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

fine grained access rights according to SQL-standard

LDAP bind authentication

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

yes

More information provided by the system vendor

News

Edge AI: The era of on-device AI
23 April 2024

In-Memory Database Use Cases
15 February 2024

Data Viewer for Objects – announcing ObjectBox Admin
14 November 2023

Vector Databases for Edge AI
9 August 2023

Vector Database Release for Flutter / Dart + Python
13 June 2023

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