DBMS > DuckDB vs. FatDB vs. Microsoft Azure Table Storage vs. RDF4J vs. Yaacomo

System Properties Comparison DuckDB vs. FatDB vs. Microsoft Azure Table Storage vs. RDF4J vs. Yaacomo

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.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

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

A Wide Column Store for rapid development using massive semi-structured datasets

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Document store
Key-value store

Wide column store

RDF store

Relational DBMS

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

Score	5.61
Rank	#60	Overall
	#34	Relational DBMS

Score	3.55
Rank	#80	Overall
	#6	Wide column stores

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Website

duckdb.org

azure.microsoft.com/en-us/services/storage/tables

rdf4j.org

yaacomo.com

Technical documentation

duckdb.org/docs

rdf4j.org/documentation

Developer

FatCloud

Microsoft

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

Q2WEB GmbH

Initial release

2018

2012

2004

2009

Current release

1.0.0, June 2024

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

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

C++

Java

Server operating systems

server-less

Windows

hosted

Linux
OS X
Unix
Windows

Android
Linux
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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.

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

RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Implicit feature of the cloud service

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

Source-replica replication

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

yes

Transaction concepts

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

ACID

optimistic locking

ACID

Isolation support depends on the API used

ACID

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

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

Can implement custom security layer via applications

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

My First Billion (of Rows) in DuckDB
1 May 2024, Towards Data Science

TigerEye Open Sources DuckDB.dart
15 August 2024, Datanami

Unleashing Postgres for Analytics With DuckDB Integration
24 July 2024, The New Stack

PostgreSQL in line for DuckDB-shaped boost in analytics arena
20 August 2024, The Register

TigerEye releases open-source DuckDB.dart to simplify data-intensive application development
15 August 2024, SiliconANGLE News

provided by Google News

How to use Azure Table storage in .Net
10 July 2024, InfoWorld

Working with Azure to Use and Manage Data Lakes
23 July 2024, Simplilearn

Azure Cosmos DB Data Migration tool imports from Azure Table storage
5 May 2015, azure.microsoft.com

How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB
9 July 2021, hackernoon.com

How to write data to Azure Table Store with an Azure Function
14 April 2017, Experts Exchange

provided by Google News

Share this page