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

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

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A MySQL compatible DBMS with Git-like versioning of data and schema

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

Document store

Spatial DBMS
Time Series 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	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Website

aws.amazon.com/redshift

github.com/dolthub/dolt
www.dolthub.com

www.kinetica.com

openmldb.ai

Technical documentation

docs.aws.amazon.com/redshift

docs.dolthub.com

docs.kinetica.com

openmldb.ai/docs/zh/main

Developer

Amazon (based on PostgreSQL)

DoltHub Inc

Kinetica

4 Paradigm Inc.

Initial release

2012

2018

2012

2020

Current release

7.1, August 2021

2024-2 February 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

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

C, C++

C++, Java, Scala

Server operating systems

hosted

Linux
macOS
Windows

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

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

CLI Client
HTTP REST

JDBC
ODBC
RESTful HTTP API

JDBC
SQLAlchemy

Supported programming languages

All languages supporting JDBC/ODBC

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C++
Java
JavaScript (Node.js)
Python

C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

currently in alpha release

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

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

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

Only one user is configurable, and must be specified in the config file at startup

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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