DBMS > Amazon Redshift vs. Hive vs. Kinetica vs. Prometheus

System Properties Comparison Amazon Redshift vs. Hive vs. Kinetica vs. Prometheus

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Fully vectorized database across both GPUs and CPUs

Open-source Time Series DBMS and monitoring system

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

aws.amazon.com/redshift

hive.apache.org

www.kinetica.com

prometheus.io

Technical documentation

docs.aws.amazon.com/redshift

cwiki.apache.org/confluence/display/Hive/Home

docs.kinetica.com

prometheus.io/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

initially developed by Facebook

Kinetica

Initial release

2012

2015

Current release

3.1.3, April 2022

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

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

C, C++

Server operating systems

hosted

All OS with a Java VM

Linux

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
PHP
Python

C++
Java
JavaScript (Node.js)
Python

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

none

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

Access rights for users and roles on table level

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