DBMS > Amazon Aurora vs. Apache Druid vs. Citus vs. Snowflake vs. Teradata

System Properties Comparison Amazon Aurora vs. Apache Druid vs. Citus vs. Snowflake vs. Teradata

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	7.57
Rank	#51	Overall
	#32	Relational DBMS

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

aws.amazon.com/rds/aurora

Technical documentation

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

druid.apache.org/docs/latest/design

docs.citusdata.com

docs.snowflake.net/manuals/index.html

docs.teradata.com

Developer

Amazon

Apache Software Foundation and contributors

Snowflake Computing Inc.

Teradata

Initial release

2015

2012

2010

2014

1984

Current release

29.0.1, April 2024

8.1, December 2018

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

AGPL, commercial license also available

commercial

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

Server operating systems

hosted

Linux
OS X
Unix

Linux

hosted

hosted
Linux

Data scheme

yes

schema-less columns are supported

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

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

SQL for querying

yes

standard, with numerous extensions

yes

SQL 2016 + extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

CLI Client
JDBC
ODBC

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

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

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

JavaScript (Node.js)
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

manual/auto, time-based

Sharding

yes

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, via HDFS, S3 or other storage engines

Source-replica replication

other methods possible by using 3rd party extensions

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

fine grained access rights according to SQL-standard

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

fine grained access rights according to SQL-standard

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