DBMS > Amazon Redshift vs. FileMaker vs. Geode vs. IRONdb vs. RDF4J

System Properties Comparison Amazon Redshift vs. FileMaker vs. Geode vs. IRONdb vs. RDF4J

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service for use with business intelligence tools

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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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

Primary database model

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

aws.amazon.com/redshift

www.claris.com/filemaker

geode.apache.org

www.circonus.com/solutions/time-series-database/

rdf4j.org

Technical documentation

docs.aws.amazon.com/redshift

www.claris.com/resources/documentation

geode.apache.org/docs

docs.circonus.com/irondb/category/getting-started

rdf4j.org/documentation

Developer

Amazon (based on PostgreSQL)

Claris

a subsidiary of Apple

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Circonus LLC.

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

Initial release

2012

1983

2002

2017

2004

Current release

19.4.1, November 2021

1.1, February 2017

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

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 and C++

Java

Server operating systems

hosted

iOS

client part only
Linux
OS X
Windows

All OS with a Java VM

the JDK (8 or later) is also required

Linux

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL-like query language (OQL)

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

JDBC
ODBC

Filemaker WebDirect
JDBC
ODBC

Java Client API
Memcached protocol
RESTful HTTP API

HTTP API

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

PHP

.Net
All JVM based languages
C++
Groovy
Java
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions

yes, in Lua

yes

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication, warm standby

since Version 14

Multi-source replication

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

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

yes, on a single node

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

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

Access rights per client and object definable

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