DBMS > Amazon SimpleDB vs. OpenQM vs. SAP HANA vs. Spark SQL

System Properties Comparison Amazon SimpleDB vs. OpenQM vs. SAP HANA vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

QpenQM is a high-performance, self-tuning, multi-value DBMS

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

Primary database model

Key-value store

Multivalue DBMS

Relational DBMS

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	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/simpledb

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

www.sap.com/products/hana.html

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/simpledb

help.sap.com/hana

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

Developer

Amazon

Rocket Software, originally Martin Phillips

SAP

Apache Software Foundation

Initial release

2007

1993

2010

2014

Current release

3.4-12

2.0 SPS07 (April 4, 2023), April 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

GPLv2, extended commercial license available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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

Server operating systems

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

schema-free

yes

with some exceptions

yes

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.

yes

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

.Net
Basic
C
Java
Objective C
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights can be defined down to the item level

yes

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