DBMS > Amazon Redshift vs. Amazon SimpleDB vs. GeoSpock vs. Hive vs. Tibero

System Properties Comparison Amazon Redshift vs. Amazon SimpleDB vs. GeoSpock vs. Hive vs. Tibero

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

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

Spatial and temporal data processing engine for extreme data scale

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

aws.amazon.com/redshift

aws.amazon.com/simpledb

geospock.com

hive.apache.org

us.tmaxsoft.com/products/tibero

Technical documentation

docs.aws.amazon.com/redshift

docs.aws.amazon.com/simpledb

cwiki.apache.org/confluence/display/Hive/Home

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Amazon (based on PostgreSQL)

Amazon

GeoSpock

Apache Software Foundation

initially developed by Facebook

TmaxSoft

Initial release

2012

2007

2012

2003

Current release

2.0, September 2019

3.1.3, April 2022

6, April 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

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

Java, Javascript

Java

C and Assembler

Server operating systems

hosted

All OS with a Java VM

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

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

restricted

yes

All columns are indexed automatically

temporal, categorical

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

ANSI SQL for query only (using Presto)

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC

JDBC
ODBC
Thrift

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

All languages supporting JDBC/ODBC

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

C++
Java
PHP
Python

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

Persistent Stored Procedure (PSM)

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding must be implemented in the application

Automatic sharding

Sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

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

planned for next version

User concepts

Access control

fine grained access rights according to SQL-standard

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

Access rights for users can be defined per table

Access rights for users, groups and roles

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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