DBMS > Amazon Redshift vs. Apache Kylin vs. Atos Standard Common Repository vs. Postgres-XL

System Properties Comparison Amazon Redshift vs. Apache Kylin vs. Atos Standard Common Repository vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Relational DBMS

Document store
Key-value store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

aws.amazon.com/redshift

kylin.apache.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.postgres-xl.org

Technical documentation

docs.aws.amazon.com/redshift

kylin.apache.org/docs

www.postgres-xl.org/documentation

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation, originally contributed from eBay Inc

Atos Convergence Creators

Initial release

2012

2015

2016

2014

since 2012, originally named StormDB

Current release

3.1.0, July 2020

1703

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla public license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

Linux

Linux
macOS

Data scheme

yes

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

optional

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

XML type, but no XML query functionality

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

ANSI SQL for queries (using Apache Calcite)

yes

distributed, parallel query execution

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

LDAP

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

Atomic execution of specific operations

ACID

MVCC

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

LDAP bind authentication

fine grained access rights according to SQL-standard

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