DBMS > Netezza vs. SAP HANA vs. Spark SQL vs. SQream DB vs. STSdb

System Properties Comparison Netezza vs. SAP HANA vs. Spark SQL vs. SQream DB vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Data warehouse and analytics appliance part of IBM PureSystems

In-memory, column based data store. Available as appliance or cloud service

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

a GPU-based, columnar RDBMS for big data analytics workloads

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

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

Website

www.ibm.com/products/netezza

www.sap.com/products/hana.html

spark.apache.org/sql

sqream.com

github.com/STSSoft/STSdb4

Technical documentation

help.sap.com/hana

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

docs.sqream.com

Developer

IBM

SAP

Apache Software Foundation

SQream Technologies

STS Soft SC

Initial release

2000

2010

2014

2017

2011

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

2022.1.6, December 2022

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Scala

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

included in appliance

Appliance or cloud-service

Linux
OS X
Windows

Linux

Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
OLE DB

JDBC
ODBC

.Net
JDBC
ODBC

.NET Client API

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

Java
Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

C#
Java

Server-side scripts

Stored procedures

yes

SQLScript, R

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

yes, utilizing Spark Core

horizontal and vertical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

Users with fine-grained authorization concept

yes

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