DBMS > Amazon Redshift vs. Kinetica vs. PouchDB vs. Snowflake vs. Splice Machine

System Properties Comparison Amazon Redshift vs. Kinetica vs. PouchDB vs. Snowflake vs. Splice Machine

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

JavaScript DBMS with an API inspired by CouchDB

Cloud-based data warehousing service for structured and semi-structured data

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Document store

Relational 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	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

docs.kinetica.com

pouchdb.com/guides

docs.snowflake.net/manuals/index.html

splicemachine.com/how-it-works

Developer

Amazon (based on PostgreSQL)

Kinetica

Apache Software Foundation

Snowflake Computing Inc.

Splice Machine

Initial release

2012

2014

Current release

7.1, August 2021

7.1.1, June 2019

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

commercial

Open Source

AGPL 3.0, commercial license available

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++

JavaScript

Java

Server operating systems

hosted

Linux

server-less, requires a JavaScript environment (browser, Node.js)

hosted

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

Secondary indexes

restricted

yes

via views

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

CLI Client
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

JavaScript

JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

View functions in JavaScript

user defined functions

yes

Java

Triggers

yes

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

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

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

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users, groups and roles according to SQL-standard

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