DBMS > Linter vs. RDFox vs. Snowflake vs. Spark SQL vs. STSdb

System Properties Comparison Linter vs. RDFox vs. Snowflake vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines

Name

Description

RDBMS for high security requirements

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

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

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

linter.ru

www.oxfordsemantic.tech

www.snowflake.com

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

docs.oxfordsemantic.tech

docs.snowflake.net/manuals/index.html

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

Developer

relex.ru

Oxford Semantic Technologies

Snowflake Computing Inc.

Apache Software Foundation

STS Soft SC

Initial release

1990

2017

2014

2011

Current release

6.0, Septermber 2022

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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 and C++

C++

Scala

Server operating systems

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
macOS
Windows

hosted

Linux
OS X
Windows

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

primitive types and user defined types (classes)

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

APIs and other access methods

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

RESTful HTTP API
SPARQL 1.1

CLI Client
JDBC
ODBC

JDBC
ODBC

.NET Client API

Supported programming languages

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
Java

JavaScript (Node.js)
Python

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

yes

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

replication via a shared file system

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

fine grained access rights according to SQL-standard

Roles, resources, and access types

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

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