DBMS > GridGain vs. RDF4J vs. Snowflake vs. Ultipa

System Properties Comparison GridGain vs. RDF4J vs. Snowflake vs. Ultipa

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

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

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

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

RDF store

Relational DBMS

Graph DBMS

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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	0.11
Rank	#338	Overall
	#32	Graph DBMS

Website

Technical documentation

www.gridgain.com/docs/index.html

rdf4j.org/documentation

docs.snowflake.net/manuals/index.html

www.ultipa.com/document

Developer

GridGain Systems, Inc.

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

Snowflake Computing Inc.

Ultipa

Initial release

2007

2004

2014

2019

Current release

GridGain 8.5.1

License

Commercial or Open Source

commercial, open source

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

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, C++, .Net, Python, REST, SQL

Java

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Unix
Windows

hosted

Data scheme

yes

RDF Schemas

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

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

CLI Client
JDBC
ODBC

RESTful HTTP API

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Java
PHP
Python

JavaScript (Node.js)
Python

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

user defined functions

Triggers

yes (cache interceptors and events)

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Role-based access control
Security Hooks for custom implementations

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

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