DBMS > Netezza vs. RDF4J vs. Spark SQL vs. Splunk vs. STSdb

System Properties Comparison Netezza vs. RDF4J vs. Spark SQL vs. Splunk vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Data warehouse and analytics appliance part of IBM PureSystems

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

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

Analytics Platform for Big Data

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	86.45
Rank	#14	Overall
	#2	Search engines

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

www.ibm.com/products/netezza

rdf4j.org

spark.apache.org/sql

www.splunk.com

github.com/STSSoft/STSdb4

Technical documentation

rdf4j.org/documentation

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

docs.splunk.com/Documentation/Splunk

Developer

IBM

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

Apache Software Foundation

Splunk Inc.

STS Soft SC

Initial release

2000

2004

2014

2003

2011

Current release

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

commercial

Limited free edition and free developer edition available

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Scala

Server operating systems

Linux

included in appliance

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Windows

Data scheme

yes

RDF Schemas

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

Splunk Search Processing Language for search commands

APIs and other access methods

JDBC
ODBC
OLE DB

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

HTTP REST

.NET Client API

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

Java
PHP
Python

Java
Python
R
Scala

C#
Java
JavaScript
PHP
Python
Ruby

C#
Java

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Isolation support depends on the API used

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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.

User concepts

Access control

Users with fine-grained authorization concept

Access rights for users and roles

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More resources

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