DBMS > Amazon Redshift vs. Derby vs. Hive vs. SiteWhere

System Properties Comparison Amazon Redshift vs. Derby vs. Hive vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

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	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

aws.amazon.com/redshift

db.apache.org/derby

hive.apache.org

github.com/sitewhere/sitewhere

Technical documentation

docs.aws.amazon.com/redshift

db.apache.org/derby/manuals/index.html

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

sitewhere1.sitewhere.io/index.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

initially developed by Facebook

SiteWhere

Initial release

2012

1997

2012

2010

Current release

10.17.1.0, November 2023

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2

Open Source

Common Public Attribution License Version 1.0

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

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

predefined scheme

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC

JDBC
ODBC
Thrift

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

Java

C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

selectable replication factor

based on HBase

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

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

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

Access rights for users, groups and roles

Users with fine-grained authorization concept

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