DBMS > DuckDB vs. FatDB vs. HEAVY.AI vs. RDF4J

System Properties Comparison DuckDB vs. FatDB vs. HEAVY.AI vs. RDF4J

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

An embeddable, in-process, column-oriented SQL OLAP RDBMS

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

Relational DBMS

RDF store

Secondary database models

Spatial DBMS

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

Score	4.63
Rank	#69	Overall
	#37	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

duckdb.org

github.com/heavyai/heavydb
www.heavy.ai

rdf4j.org

Technical documentation

duckdb.org/docs

docs.heavy.ai

rdf4j.org/documentation

Developer

FatCloud

HEAVY.AI, Inc.

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

Initial release

2018

2012

2016

2004

Current release

1.0.0, June 2024

5.10, January 2022

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

C++ and CUDA

Java

Server operating systems

server-less

Windows

Linux

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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.

Secondary indexes

yes

SQL

Support of SQL

yes

Via inetgration in SQL Server

yes

APIs and other access methods

Arrow Database Connectivity (ADBC)
CLI Client
JDBC
ODBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JDBC
ODBC
Thrift
Vega

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C#

3rd party driver
C++
Crystal

3rd party driver
Go

3rd party driver
Java
Lisp

3rd party driver
Python
R
Ruby

3rd party driver
Rust
Swift
Zig

3rd party driver

All languages supporting JDBC/ODBC/Thrift
Python

Java
PHP
Python

Server-side scripts

Stored procedures

yes

via applications

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

Can implement custom security layer via applications

fine grained access rights according to SQL-standard

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