DBMS > Firebase Realtime Database vs. Graph Engine vs. Microsoft Access vs. Oracle Berkeley DB vs. SQLite

System Properties Comparison Firebase Realtime Database vs. Graph Engine vs. Microsoft Access vs. Oracle Berkeley DB 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.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Widely used in-process key-value store

Widely used embeddable, in-process RDBMS

Primary database model

Document store

Graph DBMS
Key-value store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

firebase.google.com/products/realtime-database

www.graphengine.io

www.microsoft.com/en-us/microsoft-365/access

www.oracle.com/database/technologies/related/berkeleydb.html

www.sqlite.org

Technical documentation

firebase.google.com/docs/database

www.graphengine.io/docs/manual

developer.microsoft.com/en-us/access

docs.oracle.com/cd/E17076_05/html/index.html

www.sqlite.org/docs.html

Developer

Google

acquired by Google 2014

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Dwayne Richard Hipp

Initial release

2012

2010

1992

1994

2000

Current release

1902 (16.0.11328.20222), March 2019

18.1.40, May 2020

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

MIT License

commercial

Bundled with Microsoft Office

Open Source

commercial license available

Open Source

Public Domain

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

.NET and C

C++

C, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

hosted

.NET

Windows

Not a real database server, but making use of DLLs

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

server-less

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

yes

SQL interfaced based on SQLite is available

yes

SQL-92 is not fully supported

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Java
JavaScript
Objective-C

C#
C++
F#
Visual Basic

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

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'

yes

since Access 2010 using the ACE-engine

Triggers

Callbacks are triggered when data changes

yes

since Access 2010 using the ACE-engine

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

but no files for transaction logging

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

yes, based on authentication and database rules

a simple user-level security was built in till version Access 2003

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