DBMS > Firebase Realtime Database vs. Microsoft SQL Server vs. OpenEdge vs. Quasardb vs. SQLite

System Properties Comparison Firebase Realtime Database vs. Microsoft SQL Server vs. OpenEdge vs. Quasardb vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Microsofts flagship relational DBMS

Application development environment with integrated database management system

Distributed, high-performance timeseries database

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

firebase.google.com/products/realtime-database

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

www.progress.com/openedge

quasar.ai

www.sqlite.org

Technical documentation

firebase.google.com/docs/database

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

documentation.progress.com/output/ua/OpenEdge_latest

doc.quasar.ai/master

www.sqlite.org/docs.html

Developer

Google

acquired by Google 2014

Microsoft

Progress Software Corporation

quasardb

Dwayne Richard Hipp

Initial release

2012

1989

1984

2009

2000

Current release

SQL Server 2022, November 2022

OpenEdge 12.2, March 2020

3.14.1, January 2024

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Public Domain

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

hosted

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

BSD
Linux
OS X
Windows

server-less

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

integer and binary

yes

not rigid because of 'dynamic typing' concept.

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

with tags

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

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

JDBC
ODBC

HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Java
JavaScript
Objective-C

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

Progress proprietary ABL (Advanced Business Language)

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

limited functionality with using 'rules'

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

yes

Triggers

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning

since Version 11.4

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

Source-replica replication

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

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

via file-system locks

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

Users and groups

Cryptographically strong user authentication and audit trail

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