DBMS > Amazon Redshift vs. Apache Pinot vs. EsgynDB vs. Hive

System Properties Comparison Amazon Redshift vs. Apache Pinot vs. EsgynDB vs. Hive

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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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Primary database model

Relational 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	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Website

aws.amazon.com/redshift

pinot.apache.org

www.esgyn.cn

hive.apache.org

Technical documentation

docs.aws.amazon.com/redshift

docs.pinot.apache.org

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

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Esgyn

Apache Software Foundation

initially developed by Facebook

Initial release

2012

2015

2012

Current release

1.0.0, September 2023

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2

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++, Java

Java

Server operating systems

hosted

All OS with a Java JDK11 or higher

Linux

All OS with a Java VM

Data scheme

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC

ADO.NET
JDBC
ODBC

JDBC
ODBC
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
Python

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

selectable replication factor

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

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

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