DBMS > Amazon SimpleDB vs. DataFS vs. Google BigQuery vs. NuoDB vs. Prometheus

System Properties Comparison Amazon SimpleDB vs. DataFS vs. Google BigQuery vs. NuoDB vs. Prometheus

Editorial information provided by DB-Engines

Name

Description

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Large scale data warehouse service with append-only tables

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Open-source Time Series DBMS and monitoring system

Primary database model

Secondary database models

Graph DBMS

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

aws.amazon.com/simpledb

newdatabase.com

cloud.google.com/bigquery

www.3ds.com/nuodb-distributed-sql-database

prometheus.io

Technical documentation

docs.aws.amazon.com/simpledb

dev.mobiland.com/Overview.xsp

cloud.google.com/bigquery/docs

doc.nuodb.com

prometheus.io/docs

Developer

Amazon

Mobiland AG

Google

Dassault Systèmes

originally NuoDB, Inc.

Initial release

2007

2018

2010

2013

2015

Current release

1.1.263, October 2022

License

Commercial or Open Source

commercial

limited edition free

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

C++

Server operating systems

hosted

Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
Windows

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

Supported programming languages

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

.Net
C
C#
C++
VB.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Java, SQL

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Proprietary Sharding system

none

data is dynamically stored/cached on the nodes where it is read/written

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Managed transparently by NuoDB

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Since BigQuery is designed for querying data

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Temporary table

User concepts

Access control

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

Windows-Profile

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Standard SQL roles/ privileges, Administrative Users

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