DBMS > Amazon Redshift vs. Impala

System Properties Comparison Amazon Redshift vs. Impala

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Primary database model

Relational DBMS

Secondary database models

Document store

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

Score	21.24
Rank	#29	Overall
	#17	Relational DBMS

Score	14.26
Rank	#36	Overall
	#22	Relational DBMS

Website

aws.amazon.com/redshift

www.cloudera.com/products/open-source/apache-hadoop/impala.html

Technical documentation

docs.aws.amazon.com/redshift

www.cloudera.com/documentation/enterprise/latest/topics/impala.html

Developer

Amazon (based on PostgreSQL)

Cloudera

Initial release

2012

2013

Current release

3.3.0, August 2019

License

Commercial or Open Source

commercial

Open Source info

Apache Version 2

Cloud-based only info

Only available as a cloud service

yes

DBaaS offerings (sponsored links) info

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

hosted

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

XML support info

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

Supported programming languages

All languages supporting JDBC/ODBC

Server-side scripts info

Stored procedures

user defined functions info

in Python

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods info

Methods for storing different data on different nodes

Sharding

Replication methods info

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 info

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys info

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts info

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

ACID

Concurrency info

Support for concurrent manipulation of data

yes

Durability info

Support for making data persistent

yes

In-memory capabilities info

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts info

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles info

based on Apache Sentry and Kerberos

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