DBMS > Amazon Redshift vs. Derby vs. FileMaker

System Properties Comparison Amazon Redshift vs. Derby vs. FileMaker

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Primary database model

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Website

aws.amazon.com/redshift

db.apache.org/derby

www.claris.com/filemaker

Technical documentation

docs.aws.amazon.com/redshift

db.apache.org/derby/manuals/index.html

www.claris.com/resources/documentation

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Claris

a subsidiary of Apple

Initial release

2012

1997

1983

Current release

10.17.1.0, November 2023

19.4.1, November 2021

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

All OS with a Java VM

iOS

client part only
Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

A http query request can return the data in XML format

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

via plugins

APIs and other access methods

JDBC
ODBC

JDBC

Filemaker WebDirect
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Java

PHP

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Source-replica replication, warm standby

since Version 14

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

fine grained access rights according to SQL-standard

simple rights management via user accounts and connection to external directory services

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