DBMS > Amazon Redshift vs. Apache Impala vs. PieCloudDB vs. STSdb

System Properties Comparison Amazon Redshift vs. Apache Impala vs. PieCloudDB vs. STSdb

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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

A cloud-native analytic database platform with new technologoy for elastic MPP

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.32
Rank	#289	Overall
	#133	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

aws.amazon.com/redshift

impala.apache.org

www.openpie.com

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

OpenPie

STS Soft SC

Initial release

2012

2013

2011

Current release

4.1.0, June 2022

2.1, January 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

GPLv2, commercial license 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++

Server operating systems

hosted

Linux

hosted

Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

yes

APIs and other access methods

JDBC
ODBC

CLI Client
JDBC
ODBC

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

Java
PL/SQL
Python
R

C#
Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

none

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

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

based on Apache Sentry and Kerberos

User Roles and pluggable authentication with full SQL Standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Licensing and pricing models

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