DBMS > Databricks vs. RDF4J vs. TinkerGraph vs. YottaDB

System Properties Comparison Databricks vs. RDF4J vs. TinkerGraph vs. YottaDB

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Editorial information provided by DB-Engines

Name

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

A fast and solid embedded Key-value store

Primary database model

Document store
Relational DBMS

RDF store

Graph DBMS

Key-value store

Secondary database models

Relational DBMS

using the Octo plugin

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

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

www.databricks.com

rdf4j.org

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

yottadb.com

Technical documentation

docs.databricks.com

rdf4j.org/documentation

yottadb.com/resources/documentation

Developer

Databricks

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

YottaDB, LLC

Initial release

2013

2004

2009

2001

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

Open Source

AGPL 3.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux
OS X
Unix
Windows

Docker
Linux

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

RDF Schemas

schema-free

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

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

by using the Octo plugin

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

TinkerPop 3

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

Python
R
Scala

Java
PHP
Python

Groovy
Java

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Isolation support depends on the API used

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

optional

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

Users and groups based on OS-security mechanisms

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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More resources

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