DBMS > Amazon Aurora vs. Amazon Redshift vs. Apache Impala vs. Ehcache

System Properties Comparison Amazon Aurora vs. Amazon Redshift vs. Apache Impala vs. Ehcache

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Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

A widely adopted Java cache with tiered storage options

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Website

aws.amazon.com/rds/aurora

aws.amazon.com/redshift

impala.apache.org

www.ehcache.org

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

www.ehcache.org/documentation

Developer

Amazon

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Terracotta Inc, owned by Software AG

Initial release

2015

2012

2013

2009

Current release

4.1.0, June 2022

3.10.0, March 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2; commercial licenses available

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

C++

Java

Server operating systems

hosted

Linux

All OS with a Java VM

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.

yes

Secondary indexes

yes

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

ADO.NET
JDBC
ODBC

JDBC
ODBC

JCache

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC

Java

Server-side scripts

Stored procedures

yes

user defined functions

in Python

yes

user defined functions and integration of map-reduce

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

by using Terracotta Server

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

selectable replication factor

yes

by using Terracotta Server

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

Tunable Consistency (Strong, Eventual, Weak)

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

based on Apache Sentry and Kerberos

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