DBMS > Amazon Redshift vs. GeoSpock vs. Kinetica vs. OpenMLDB

System Properties Comparison Amazon Redshift vs. GeoSpock vs. Kinetica vs. OpenMLDB

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Spatial and temporal data processing engine for extreme data scale

Fully vectorized database across both GPUs and CPUs

An open-source machine learning database that provides a feature platform for training and inference

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Time Series DBMS

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.11
Rank	#339	Overall
	#30	Time Series DBMS

Website

aws.amazon.com/redshift

geospock.com

www.kinetica.com

openmldb.ai

Technical documentation

docs.aws.amazon.com/redshift

docs.kinetica.com

openmldb.ai/docs/zh/main

Developer

Amazon (based on PostgreSQL)

GeoSpock

Kinetica

4 Paradigm Inc.

Initial release

2012

2020

Current release

2.0, September 2019

7.1, August 2021

2024-2 February 2024

License

Commercial or Open Source

commercial

Open Source

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, Javascript

C, C++

C++, Java, Scala

Server operating systems

hosted

Linux

Data scheme

yes

Fixed schema

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.

Secondary indexes

restricted

temporal, categorical

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

ANSI SQL for query only (using Presto)

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

JDBC

JDBC
ODBC
RESTful HTTP API

JDBC
SQLAlchemy

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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

GPU vRAM or System RAM

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per table

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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