DBMS > atoti vs. Microsoft Azure Table Storage vs. RDF4J vs. TerminusDB

System Properties Comparison atoti vs. Microsoft Azure Table Storage vs. RDF4J vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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.

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Document store
RDF store

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

atoti.io

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

rdf4j.org

terminusdb.com

Technical documentation

docs.atoti.io

rdf4j.org/documentation

terminusdb.github.io/terminusdb/#

Developer

ActiveViam

Microsoft

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

DataChemist Ltd.

Initial release

2012

2004

2018

Current release

11.0.0, January 2023

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GPL V3

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

Prolog, Rust

Server operating systems

hosted

Linux
OS X
Unix
Windows

Linux

Data scheme

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

Multidimensional Expressions (MDX)

SQL-like query language (WOQL)

APIs and other access methods

RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

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

Java
PHP
Python

JavaScript
Python

Server-side scripts

Stored procedures

Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Implicit feature of the cloud service

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

none

Journaling Streams

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

Transaction concepts

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

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 journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights based on private key authentication or shared access signatures

Role-based access control

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