DBMS > Amazon Aurora vs. EXASOL vs. MonetDB vs. Snowflake vs. Spark SQL

System Properties Comparison Amazon Aurora vs. EXASOL vs. MonetDB vs. Snowflake vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A relational database management system that stores data in columns

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	8.50
Rank	#49	Overall
	#31	Relational DBMS

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	1.72
Rank	#148	Overall
	#68	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/rds/aurora

Technical documentation

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

www.exasol.com/resources

www.monetdb.org/Documentation

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon

Exasol

MonetDB BV

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2015

2000

2004

2014

Current release

Dec2023 (11.49), December 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

commercial

Open Source

Apache 2.0

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

Scala

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

hosted

Linux
OS X
Windows

Data scheme

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

CLI Client
JDBC
ODBC

JDBC
ODBC

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

Java
Lua
Python
R

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions

yes, in SQL, C, R

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Sharding via remote tables

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication available in experimental status

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

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

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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

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