DBMS > Databricks vs. GraphDB vs. PostGIS vs. SWC-DB

System Properties Comparison Databricks vs. GraphDB vs. PostGIS vs. SWC-DB

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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.

Enterprise-ready RDF and graph database with efficient reasoning, cluster and external index synchronization support. It supports also SQL JDBC access to Knowledge Graph and GraphQL over SPARQL.

Spatial extension of PostgreSQL

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Relational DBMS

Graph DBMS
RDF store

Spatial DBMS

Wide column store

Secondary database models

Relational DBMS

Time Series 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	3.32
Rank	#91	Overall
	#6	Graph DBMS
	#4	RDF stores

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.databricks.com

www.ontotext.com

postgis.net

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.databricks.com

graphdb.ontotext.com/documentation

postgis.net/documentation

Social network pages

Developer

Databricks

Ontotext

Alex Kashirin

Initial release

2013

2000

2005

2020

Current release

10.4, October 2023

3.4.2, February 2024

0.5, April 2021

License

Commercial or Open Source

commercial

Some plugins of GraphDB Workbench are open sourced

Open Source

GPL v2.0

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

C++

Server operating systems

hosted

All OS with a Java VM
Linux
OS X
Windows

Linux

Data scheme

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

schema-free and OWL/RDFS-schema support; RDF shapes

yes

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

yes, supports real-time synchronization and indexing in SOLR/Elastic search/Lucene and GeoSPARQL geometry data indexes

yes

SQL

Support of SQL

with Databricks SQL

stored SPARQL accessed as SQL using Apache Calcite through JDBC/ODBC

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

GeoSPARQL
GraphQL
GraphQL Federation
Java API
JDBC
RDF4J API
RDFS
RIO
Sail API
Sesame REST HTTP Protocol
SPARQL 1.1

Proprietary protocol
Thrift

Supported programming languages

Python
R
Scala

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

C++

Server-side scripts

Stored procedures

user defined functions and aggregates

well-defined plugin interfaces; JavaScript server-side extensibility

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

based on PostgreSQL

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

yes

based on PostgreSQL

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency, Eventual consistency (configurable in cluster mode per master or individual client request)

Immediate Consistency

Foreign keys

Referential integrity

yes

Constraint checking

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

Default Basic authentication through RDF4J client, or via Java when run with cURL, default token-based in the Workbench or via Rest API, optional access through OpenID or Kerberos single sign-on.

yes

based on PostgreSQL

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
» more

Ontotext GraphDB is a semantic database engine that allows organizations to build...
» more

Competitive advantages

GraphDB allows you to link text and data in big knowledge graphs. It’s easy to experiment...
» more

Typical application scenarios

Metadata enrichment and management, linked data publishing, semantic inferencing...
» more

Key customers

GraphDB provides a platform for building next-generation AI and Knowledge Graph...
» more

Market metrics

GraphDB is the most utilized semantic triplestore for mission-critical enterprise...
» more

Licensing and pricing models

GraphDB Free is a non-commercial version and is free to use. GraphDB Enterprise edition...
» more

News

Migrating From LPG to RDF Graph Model
8 May 2024

Case study: Policy Enforcement Automation With Semantics
2 May 2024

Okay, RAG… We Have a Problem
26 April 2024

Scaling Understanding with the Help of Feedback Loops, Knowledge Graphs and NLP
19 April 2024

How Knowledge Graphs Power Data Mesh and Data Fabric
10 April 2024

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

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More resources

DB-Engines blog posts

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