DBMS > Amazon Redshift vs. Apache Kylin vs. BigObject vs. Greenplum

System Properties Comparison Amazon Redshift vs. Apache Kylin vs. BigObject vs. Greenplum

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Editorial information provided by DB-Engines

Name

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

Analytic DBMS for real-time computations and queries

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	1.20
Rank	#175	Overall
	#80	Relational DBMS

Score	0.13
Rank	#342	Overall
	#150	Relational DBMS

Score	9.23
Rank	#47	Overall
	#30	Relational DBMS

Website

aws.amazon.com/redshift

kylin.apache.org

bigobject.io

greenplum.org

Technical documentation

docs.aws.amazon.com/redshift

kylin.apache.org/docs

docs.bigobject.io

docs.greenplum.org

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation, originally contributed from eBay Inc

BigObject, Inc.

Pivotal Software Inc.

Initial release

2012

2015

2005

Current release

3.1.0, July 2020

7.0.0, September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free community edition available

Open Source

Apache 2.0

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

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Data scheme

yes

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

since Version 4.2

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

ANSI SQL for queries (using Apache Calcite)

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

fluentd
ODBC
RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
Java
Perl
Python
R

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

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