DBMS > Databricks vs. FeatureBase vs. RDF4J vs. TinkerGraph

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

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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.

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

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

Primary database model

Document store
Relational DBMS

Relational DBMS

RDF store

Graph DBMS

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	0.22
Rank	#309	Overall
	#139	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

www.databricks.com

www.featurebase.com

rdf4j.org

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

Technical documentation

docs.databricks.com

docs.featurebase.com

rdf4j.org/documentation

Developer

Databricks

Molecula and Pilosa Open Source Contributors

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

Initial release

2013

2017

2004

2009

Current release

2022, May 2022

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

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

Server operating systems

hosted

Linux
macOS

Linux
OS X
Unix
Windows

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

SQL queries

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

gRPC
JDBC
Kafka Connector
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

TinkerPop 3

Supported programming languages

Python
R
Scala

Java
Python

Java
PHP
Python

Groovy
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

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

yes

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

yes

in-memory storage is supported as well

optional

In-memory capabilities

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

yes

User concepts

Access control

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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