DBMS > Apache Drill vs. FileMaker vs. Google Cloud Firestore vs. Microsoft SQL Server vs. PostGIS

System Properties Comparison Apache Drill vs. FileMaker vs. Google Cloud Firestore vs. Microsoft SQL Server vs. PostGIS

Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

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.

Microsofts flagship relational DBMS

Spatial extension of PostgreSQL

Primary database model

Document store
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Website

drill.apache.org

www.claris.com/filemaker

firebase.google.com/products/firestore

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

postgis.net

Technical documentation

drill.apache.org/docs

www.claris.com/resources/documentation

firebase.google.com/docs/firestore

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

postgis.net/documentation

Developer

Apache Software Foundation

Claris

a subsidiary of Apple

Google

Microsoft

Initial release

2012

1983

2017

1989

2005

Current release

1.20.3, January 2023

19.4.1, November 2021

SQL Server 2022, November 2022

3.4.2, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

restricted free version is available

Open Source

GPL v2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

Linux
OS X
Windows

iOS

client part only
Linux
OS X
Windows

hosted

Linux
Windows

Data scheme

schema-free

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.

yes

A http query request can return the data in XML format

yes

Secondary indexes

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

yes

via plugins

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Filemaker WebDirect
JDBC
ODBC

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

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

Supported programming languages

C++

PHP

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

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

Server-side scripts

Stored procedures

user defined functions

yes

yes, Firebase Rules & Cloud Functions

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

user defined functions

Triggers

yes

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

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

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication, warm standby

since Version 14

Multi-source replication

yes, but depending on the SQL-Server Edition

yes

based on PostgreSQL

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

Depending on the underlying data source

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

User concepts

Access control

Depending on the underlying data source

simple rights management via user accounts and connection to external directory services

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

yes

based on PostgreSQL

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