DBMS > Amazon Aurora vs. BigObject vs. DataFS vs. Google Cloud Datastore

System Properties Comparison Amazon Aurora vs. BigObject vs. DataFS vs. Google Cloud Datastore

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Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Analytic DBMS for real-time computations and queries

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.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Object oriented DBMS

Document store

Secondary database models

Document store

Graph DBMS

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Website

aws.amazon.com/rds/aurora

bigobject.io

newdatabase.com

cloud.google.com/datastore

Technical documentation

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

docs.bigobject.io

dev.mobiland.com/Overview.xsp

cloud.google.com/datastore/docs

Developer

Amazon

BigObject, Inc.

Mobiland AG

Google

Initial release

2015

2018

2008

Current release

1.1.263, October 2022

License

Commercial or Open Source

commercial

free community edition 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.

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Windows

hosted

Data scheme

yes

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

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

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-like DML and DDL statements

SQL-like query language (GQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

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#
C++
VB.Net

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

Server-side scripts

Stored procedures

yes

Lua

using Google App Engine

Triggers

yes

no, except callback-events from server when changes happened

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Proprietary Sharding system

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Windows-Profile

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

More information provided by the system vendor

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

Amazon - the rising star in the DBMS market
3 August 2015, Matthias Gelbmann

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