DBMS > EXASOL vs. GraphDB vs. JanusGraph vs. Snowflake

System Properties Comparison EXASOL vs. GraphDB vs. JanusGraph vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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.

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

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

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	3.32
Rank	#91	Overall
	#6	Graph DBMS
	#4	RDF stores

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

Technical documentation

www.exasol.com/resources

graphdb.ontotext.com/documentation

docs.janusgraph.org

docs.snowflake.net/manuals/index.html

Social network pages

Developer

Exasol

Ontotext

Linux Foundation; originally developed as Titan by Aurelius

Snowflake Computing Inc.

Initial release

2000

2017

2014

Current release

10.4, October 2023

0.6.3, February 2023

License

Commercial or Open Source

commercial

Some plugins of GraphDB Workbench are open sourced

Open Source

Apache 2.0

commercial

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

All OS with a Java VM
Linux
OS X
Windows

Linux
OS X
Unix
Windows

hosted

Data scheme

yes

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

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes

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

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

CLI Client
JDBC
ODBC

Supported programming languages

Java
Lua
Python
R

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

Clojure
Java
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

well-defined plugin interfaces; JavaScript server-side extensibility

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

via Faunus, a graph analytics engine

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)

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Constraint checking

yes

Relationships in graphs

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

Access rights for users, groups and roles according to SQL-standard

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.

User authentification and security via Rexster Graph Server

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

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

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

Building Smarter Financial Services: The Role of Semantic Technologies, Knowledge Graphs and Generative AI
3 April 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.