DBMS > Google Cloud Firestore vs. IBM Db2 warehouse vs. Microsoft SQL Server vs. MonetDB vs. SAP SQL Anywhere

System Properties Comparison Google Cloud Firestore vs. IBM Db2 warehouse vs. Microsoft SQL Server vs. MonetDB vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

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.

Cloud-based data warehousing service

Microsofts flagship relational DBMS

A relational database management system that stores data in columns

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Document store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

firebase.google.com/products/firestore

www.ibm.com/products/db2/warehouse

www.microsoft.com/en-us/sql-server

www.monetdb.org

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

firebase.google.com/docs/firestore

learn.microsoft.com/en-US/sql/sql-server

www.monetdb.org/Documentation

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Google

IBM

Microsoft

MonetDB BV

SAP

formerly Sybase

Initial release

2017

2014

1989

2004

1992

Current release

SQL Server 2022, November 2022

Dec2023 (11.49), December 2023

17, July 2015

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Mozilla Public License 2.0

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
Windows

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

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.

Import/export of XML data possible

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes

APIs and other access methods

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

.NET Client API
JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

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

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

PL/SQL, SQL PL

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes, in SQL, C, R

yes, in C/C++, Java, .Net or Perl

Triggers

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

yes, but depending on the SQL-Server Edition

none

Source-replica replication available in experimental status

Source-replica replication

Database mirroring

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

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