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

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

Editorial information provided by DB-Engines

Name

Description

Data warehouse and analytics appliance part of IBM PureSystems

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

YTsaurus is an open source platform for distributed storage and processing.

Primary database model

Relational DBMS

Search engine

Key-value store

Document store
Key-value store

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

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

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

Score	0.11
Rank	#340	Overall
	#45	Document stores
	#50	Key-value stores

Website

www.ibm.com/products/netezza

spark.apache.org/sql

www.splunk.com

github.com/STSSoft/STSdb4

ytsaurus.tech

Technical documentation

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

docs.splunk.com/Documentation/Splunk

ytsaurus.tech/docs/en

Developer

IBM

Apache Software Foundation

Splunk Inc.

STS Soft SC

Yandex

Initial release

2000

2014

2003

2011

2023

Current release

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Limited free edition and free developer edition available

Open Source

GPLv2, commercial license available

Open Source

Apache License, Version 2.0

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

Scala

C++

Server operating systems

Linux

included in appliance

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Windows

Ubuntu

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

Splunk Search Processing Language for search commands

YQL, an SQL-based language, is supported

APIs and other access methods

JDBC
ODBC
OLE DB

JDBC
ODBC

HTTP REST

.NET Client API

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

Java
Python
R
Scala

C#
Java
JavaScript
PHP
Python
Ruby

C#
Java

C++
Go
Java
JavaScript
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication

none

yes

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

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

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.

User concepts

Access control

Users with fine-grained authorization concept

Access rights for users and roles

Access Control Lists

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Enterprise Search Engines almost double their popularity in the last 12 months
2 July 2014, Paul Andlinger

show all

Recent citations in the news

IBM announces availability of the high-performance, cloud-native Netezza Performance Server as a Service on AWS
11 July 2023, IBM

AWS and IBM Netezza come out in support of Iceberg in table format face-off
1 August 2023, The Register

Migrating your Netezza data warehouse to Amazon Redshift | Amazon Web Services
27 May 2020, AWS Blog

U.S. Navy Chooses Yellowbrick, Sunsets IBM Netezza
22 March 2023, Business Wire

IBM Brings Back a Netezza, Attacks Yellowbrick
29 June 2020, Datanami

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

Performant IPv4 Range Spark Joins | by Jean-Claude Cote
24 January 2024, Towards Data Science

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

provided by Google News

Share this page