DBMS > Amazon Redshift vs. Kinetica vs. Spark SQL vs. Warp 10

System Properties Comparison Amazon Redshift vs. Kinetica vs. Spark SQL vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Fully vectorized database across both GPUs and CPUs

Spark SQL is a component on top of 'Spark Core' for structured data processing

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

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	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

aws.amazon.com/redshift

www.kinetica.com

spark.apache.org/sql

www.warp10.io

Technical documentation

docs.aws.amazon.com/redshift

docs.kinetica.com

spark.apache.org/docs/latest/sql-programming-guide.html

www.warp10.io/content/02_Getting_started

Developer

Amazon (based on PostgreSQL)

Kinetica

Apache Software Foundation

SenX

Initial release

2012

2014

2015

Current release

7.1, August 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache License 2.0

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++

Scala

Java

Server operating systems

hosted

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

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

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

WarpScript

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

yes, utilizing Spark Core

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

selectable replication factor

based on HBase

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

based on HBase

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 and roles on table level

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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