DBMS > BigObject vs. Coveo vs. FileMaker vs. RDF4J vs. Stardog

System Properties Comparison BigObject vs. Coveo vs. FileMaker vs. RDF4J vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

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

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

bigobject.io

www.coveo.com

www.claris.com/filemaker

rdf4j.org

www.stardog.com

Technical documentation

docs.bigobject.io

docs.coveo.com

www.claris.com/resources/documentation

rdf4j.org/documentation

docs.stardog.com

Developer

BigObject, Inc.

Coveo

Claris

a subsidiary of Apple

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

Stardog-Union

Initial release

2015

2012

1983

2004

2010

Current release

19.4.1, November 2021

7.3.0, May 2020

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

iOS

client part only
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux
macOS
Windows

Data scheme

yes

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

RDF Schemas

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like DML and DDL statements

yes

via plugins

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

RESTful HTTP API

Filemaker WebDirect
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C#
Java
JavaScript
Python

PHP

Java
PHP
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

Lua

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Source-replica replication, warm standby

since Version 14

none

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

relationships in graphs

Transaction concepts

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

yes

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

granular access controls, API key management, content filters

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

Access rights for users and roles

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