DBMS > Amazon Redshift vs. Apache Hive vs. DataFS

System Properties Comparison Amazon Redshift vs. Apache Hive vs. DataFS

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Primary database model

Relational DBMS

Object oriented DBMS

Secondary database models

Graph DBMS

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

Score	14.70
Rank	#36	Overall
	#22	Relational DBMS

Score	75.77
Rank	#16	Overall
	#11	Relational DBMS

Score	0.00
Rank	#385	Overall
	#21	Object oriented DBMS

Website

aws.amazon.com/redshift

hive.apache.org

newdatabase.com

Technical documentation

docs.aws.amazon.com/redshift

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

dev.mobiland.com/Overview.xsp

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

initially developed by Facebook

Mobiland AG

Initial release

2012

2018

Current release

3.1.3, April 2022

1.1.263, October 2022

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

Server operating systems

hosted

All OS with a Java VM

Windows

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift

.NET Client API
Proprietary client DLL
WinRT client

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
PHP
Python

.Net
C
C#
C++
VB.Net

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

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

Windows-Profile

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