DBMS > Amazon Redshift vs. Google Cloud Firestore vs. Microsoft Azure Table Storage vs. mSQL

System Properties Comparison Amazon Redshift vs. Google Cloud Firestore vs. Microsoft Azure Table Storage vs. mSQL

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

A Wide Column Store for rapid development using massive semi-structured datasets

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Website

aws.amazon.com/redshift

firebase.google.com/products/firestore

azure.microsoft.com/en-us/services/storage/tables

hughestech.com.au/products/msql

Technical documentation

docs.aws.amazon.com/redshift

firebase.google.com/docs/firestore

Developer

Amazon (based on PostgreSQL)

Google

Microsoft

Hughes Technologies

Initial release

2012

2017

2012

1994

Current release

4.4, October 2021

License

Commercial or Open Source

commercial

free licenses can be provided

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

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

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

user defined functions

in Python

yes, Firebase Rules & Cloud Functions

Triggers

yes, with Cloud Functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

yes

optimistic locking

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 based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

Access rights based on private key authentication or shared access signatures

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