DBMS > Amazon Aurora vs. Amazon DynamoDB vs. Netezza vs. Sadas Engine vs. Teradata Aster

System Properties Comparison Amazon Aurora vs. Amazon DynamoDB vs. Netezza vs. Sadas Engine vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

Data warehouse and analytics appliance part of IBM PureSystems

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Platform for big data analytics on multistructured data sources and types

Primary database model

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	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

aws.amazon.com/rds/aurora

aws.amazon.com/dynamodb

www.ibm.com/products/netezza

www.sadasengine.com

Technical documentation

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

docs.aws.amazon.com/dynamodb

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Amazon

IBM

SADAS s.r.l.

Teradata

Initial release

2015

2012

2000

2006

2005

Current release

8.0

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

free trial version available

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++

Server operating systems

hosted

Linux

included in appliance

AIX
Linux
Windows

Linux

Data scheme

yes

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

JDBC
ODBC
OLE DB

JDBC
ODBC
Proprietary protocol

ADO.NET
JDBC
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

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C++
Fortran
Java
Lua
Perl
Python
R

.Net
C
C#
C++
Groovy
Java
PHP
Python

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

R packages

Triggers

yes

by integration with AWS Lambda

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Source-replica replication

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

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

managed by 'Learn by Usage'

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)

Users with fine-grained authorization concept

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

fine grained access rights according to SQL-standard

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