DBMS > RDFox vs. Snowflake vs. Spark SQL vs. Yaacomo

System Properties Comparison RDFox vs. Snowflake vs. Spark SQL vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

High performance knowledge graph and semantic reasoning engine

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.29
Rank	#300	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.oxfordsemantic.tech

www.snowflake.com

spark.apache.org/sql

yaacomo.com

Technical documentation

docs.oxfordsemantic.tech

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Oxford Semantic Technologies

Snowflake Computing Inc.

Apache Software Foundation

Q2WEB GmbH

Initial release

2017

2014

2009

Current release

6.0, Septermber 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

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

C++

Scala

Server operating systems

Linux
macOS
Windows

hosted

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

yes

RDF schemas

yes

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

yes

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

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API
SPARQL 1.1

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C
Java

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

Triggers

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

yes

yes, utilizing Spark Core

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

replication via a shared file system

yes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Immediate Consistency

Foreign keys

Referential integrity

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.

yes

User concepts

Access control

Roles, resources, and access types

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

fine grained access rights according to SQL-standard

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