DBMS > IBM Db2 warehouse vs. OushuDB vs. SAP IQ vs. Spark SQL vs. SWC-DB

System Properties Comparison IBM Db2 warehouse vs. OushuDB vs. SAP IQ vs. Spark SQL vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Cloud-based data warehousing service

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Columnar RDBMS optimized for Big Data analytics

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

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

column-oriented

Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.ibm.com/products/db2/warehouse

www.oushu.com/product/oushuDB

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

spark.apache.org/sql

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

www.oushu.com/documentation

help.sap.com/docs/SAP_IQ

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

Developer

IBM

Oushu

SAP, formerly Sybase

Apache Software Foundation

Alex Kashirin

Initial release

2014

1994

2014

2020

Current release

4.0.1, August 2020

16.1 SPS04, April 2019

3.5.0 ( 2.13), September 2023

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPL V3

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

Scala

C++

Server operating systems

hosted

Linux

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

Full-featured ANSI SQL support

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

.NET Client API
JDBC
ODBC
OLE DB

JDBC
ODBC

ADO.NET
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C
C++

C
C#
C++
Java
Perl
PHP
Python
Ruby

Java
Python
R
Scala

C++

Server-side scripts

Stored procedures

PL/SQL, SQL PL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

shared disk or shared nothing architectures with SAP IQ Multiplexer

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

SAP/Sybase Replication Server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

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

fine grained access rights according to SQL-standard

Kerberos, SSL and role based access

fine grained access rights according to SQL-standard

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