DBMS > Amazon Aurora vs. Amazon Redshift vs. EsgynDB vs. H2GIS vs. Kinetica

System Properties Comparison Amazon Aurora vs. Amazon Redshift vs. EsgynDB vs. H2GIS vs. Kinetica

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Large scale data warehouse service for use with business intelligence tools

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

Spatial extension of H2

Fully vectorized database across both GPUs and CPUs

Primary database model

Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Document store

Relational DBMS

Spatial DBMS
Time Series DBMS

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

Score	8.50
Rank	#49	Overall
	#31	Relational DBMS

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	0.23
Rank	#319	Overall
	#141	Relational DBMS

Score	0.05
Rank	#372	Overall
	#7	Spatial DBMS

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Website

aws.amazon.com/rds/aurora

aws.amazon.com/redshift

www.esgyn.cn

www.h2gis.org

www.kinetica.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

docs.aws.amazon.com/redshift

www.h2gis.org/docs/home

docs.kinetica.com

Developer

Amazon

Amazon (based on PostgreSQL)

Esgyn

CNRS

Kinetica

Initial release

2015

2012

2015

2013

2012

Current release

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

LGPL 3.0

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.

Implementation language

C++, Java

Java

C, C++

Server operating systems

hosted

Linux

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.

yes

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

All languages supporting JDBC/ODBC

All languages supporting JDBC/ODBC/ADO.Net

Java

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

in Python

Java Stored Procedures

yes

based on H2

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

horizontal partitioning

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Multi-source replication between multi datacenters

yes

based on H2

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

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

yes

based on H2

Access rights for users and roles on table level

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