DBMS > Amazon Redshift vs. Apache Pinot vs. Kinetica vs. SingleStore vs. Snowflake

System Properties Comparison Amazon Redshift vs. Apache Pinot vs. Kinetica vs. SingleStore vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

Fully vectorized database across both GPUs and CPUs

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

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

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

docs.pinot.apache.org

docs.kinetica.com

docs.singlestore.com

docs.snowflake.net/manuals/index.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Kinetica

SingleStore Inc.

Snowflake Computing Inc.

Initial release

2012

2015

2012

2013

2014

Current release

1.0.0, September 2023

7.1, August 2021

8.5, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free developer edition available

commercial

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.

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Implementation language

Java

C, C++

C++, Go

Server operating systems

hosted

All OS with a Java JDK11 or higher

Linux

64 bit version required

hosted

Data scheme

yes

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

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like DML and DDL statements

yes

but no triggers and foreign keys

yes

APIs and other access methods

JDBC
ODBC

JDBC

JDBC
ODBC
RESTful HTTP API

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
Python

C++
Java
JavaScript (Node.js)
Python

Bash
C
C#
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

user defined functions

Triggers

yes

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

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

hash partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

stores two copies of each physical data partition on two separate nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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

Fine grained access control via users, groups and roles

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

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
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