DBMS > Amazon Aurora vs. Amazon Redshift vs. Databend vs. Quasardb

System Properties Comparison Amazon Aurora vs. Amazon Redshift vs. Databend vs. Quasardb

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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

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store

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

Score	7.57
Rank	#51	Overall
	#32	Relational DBMS

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

aws.amazon.com/rds/aurora

aws.amazon.com/redshift

github.com/datafuselabs/databend
www.databend.com

quasar.ai

Technical documentation

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

docs.aws.amazon.com/redshift

docs.databend.com

doc.quasar.ai/master

Developer

Amazon

Amazon (based on PostgreSQL)

Databend Labs

quasardb

Initial release

2015

2012

2021

2009

Current release

1.0.59, April 2023

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

Rust

C++

Server operating systems

hosted

hosted
Linux
macOS

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

CLI Client
JDBC
RESTful HTTP API

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

Go
Java
JavaScript (Node.js)
Python
Rust

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

user defined functions

in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles

Cryptographically strong user authentication and audit trail

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