DBMS > Amazon Redshift vs. Greenplum vs. GreptimeDB vs. Sadas Engine

System Properties Comparison Amazon Redshift vs. Greenplum vs. GreptimeDB vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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.

An open source Time Series DBMS built for increased scalability, high performance and efficiency

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

Score	0.12
Rank	#351	Overall
	#34	Time Series DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

aws.amazon.com/redshift

greenplum.org

greptime.com

www.sadasengine.com

Technical documentation

docs.aws.amazon.com/redshift

docs.greenplum.org

docs.greptime.com

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Amazon (based on PostgreSQL)

Pivotal Software Inc.

Greptime Inc.

SADAS s.r.l.

Initial release

2012

2005

2022

2006

Current release

7.0.0, September 2023

8.0

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

free trial version 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

Rust

C++

Server operating systems

hosted

Linux

Android
Docker
FreeBSD
Linux
macOS
Windows

AIX
Linux
Windows

Data scheme

yes

schema-free, schema definition possible

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

yes

APIs and other access methods

JDBC
ODBC

gRPC
HTTP API
JDBC

JDBC
ODBC
Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

C
Java
Perl
Python
R

C++
Erlang
Go
Java
JavaScript

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

Simple rights management via user accounts

Access rights for users, groups and roles according to 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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Market metrics

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Licensing and pricing models

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