DBMS > Amazon Redshift vs. atoti vs. Coveo vs. Greenplum vs. SpaceTime

System Properties Comparison Amazon Redshift vs. atoti vs. Coveo vs. Greenplum vs. SpaceTime

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

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.

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Secondary database models

Document store
Spatial DBMS

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Website

aws.amazon.com/redshift

atoti.io

www.coveo.com

greenplum.org

www.mireo.com/spacetime

Technical documentation

docs.aws.amazon.com/redshift

docs.atoti.io

docs.coveo.com

docs.greenplum.org

Developer

Amazon (based on PostgreSQL)

ActiveViam

Coveo

Pivotal Software Inc.

Mireo

Initial release

2012

2005

2020

Current release

7.0.0, September 2023

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

Apache 2.0

commercial

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

C++

Server operating systems

hosted

Linux

Data scheme

yes

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

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

Multidimensional Expressions (MDX)

yes

A subset of ANSI SQL is implemented

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

JDBC
ODBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C#
Java
JavaScript
Python

C
Java
Perl
Python
R

C#
C++
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

yes

Sharding

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Real-time block device replication (DRBD)

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

granular access controls, API key management, content filters

fine grained access rights according to SQL-standard

yes

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