DBMS > Apache Pinot vs. atoti vs. RDF4J

System Properties Comparison Apache Pinot vs. atoti vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

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

Primary database model

Relational DBMS

Object oriented DBMS

RDF store

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

Score	0.58
Rank	#244	Overall
	#111	Relational DBMS

Score	0.59
Rank	#242	Overall
	#10	Object oriented DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Website

pinot.apache.org

atoti.io

rdf4j.org

Technical documentation

docs.pinot.apache.org

docs.atoti.io

rdf4j.org/documentation

Developer

Apache Software Foundation and contributors

ActiveViam

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

Initial release

2015

2004

Current release

1.0.0, September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free versions available

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java JDK11 or higher

Linux
OS X
Unix
Windows

Data scheme

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

Multidimensional Expressions (MDX)

APIs and other access methods

JDBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Go
Java
Python

Java
PHP
Python

Server-side scripts

Stored procedures

Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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More resources

Recent citations in the news

Real-Time Analytics for Mobile App Crashes using Apache Pinot
2 November 2023, Uber

Speed of Apache Pinot at the Cost of Cloud Object Storage with Tiered Storage
16 August 2023, InfoQ.com

StarTree Announces Integration between Apache Pinot and Delta Lake with StarTree Cloud
20 June 2023, Datanami

StarTree brings Apache Pinot real-time database to the cloud
22 March 2022, TechTarget

Data analytics startup StarTree secures cash to expand its Apache Pinot-powered platform
29 August 2022, TechCrunch

provided by Google News

FRTB product of the year: ActiveViam
28 November 2023, Risk.net

provided by Google News

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