DBMS > Amazon Aurora vs. Amazon SimpleDB vs. EXASOL vs. FatDB vs. Snowflake

System Properties Comparison Amazon Aurora vs. Amazon SimpleDB vs. EXASOL vs. FatDB vs. Snowflake

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Document store
Key-value store

Relational DBMS

Secondary database models

Document store

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

Score	8.50
Rank	#49	Overall
	#31	Relational DBMS

Score	1.88
Rank	#139	Overall
	#24	Key-value stores

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

aws.amazon.com/rds/aurora

aws.amazon.com/simpledb

www.exasol.com

www.snowflake.com

Technical documentation

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

docs.aws.amazon.com/simpledb

www.exasol.com/resources

docs.snowflake.net/manuals/index.html

Developer

Amazon

Exasol

FatCloud

Snowflake Computing Inc.

Initial release

2015

2007

2000

2012

2014

License

Commercial or Open Source

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

Server operating systems

hosted

Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

All columns are indexed automatically

yes

SQL

Support of SQL

yes

Via inetgration in SQL Server

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

.Net
JDBC
ODBC
WebSocket

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

CLI Client
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

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

Java
Lua
Python
R

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

yes

via applications

user defined functions

Triggers

yes

via applications

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding must be implemented in the application

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrent data updates can be detected by the application

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 and roles can be defined via the AWS Identity and Access Management (IAM)

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

Can implement custom security layer via applications

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

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