DBMS > Blueflood vs. Derby vs. Firebase Realtime Database vs. Microsoft Access vs. Microsoft Azure Cosmos DB

System Properties Comparison Blueflood vs. Derby vs. Firebase Realtime Database vs. Microsoft Access vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

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.)

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

blueflood.io

db.apache.org/derby

firebase.google.com/products/realtime-database

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

azure.microsoft.com/services/cosmos-db

Technical documentation

github.com/rax-maas/blueflood/wiki

db.apache.org/derby/manuals/index.html

firebase.google.com/docs/database

developer.microsoft.com/en-us/access

learn.microsoft.com/azure/cosmos-db

Developer

Rackspace

Apache Software Foundation

Google

acquired by Google 2014

Microsoft

Initial release

2013

1997

2012

1992

2014

Current release

10.17.1.0, November 2023

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

Bundled with Microsoft Office

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

Java

C++

Server operating systems

Linux
OS X

All OS with a Java VM

hosted

Windows

Not a real database server, but making use of DLLs

hosted

Data scheme

predefined scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

yes

but not compliant to any SQL standard

SQL-like query language

APIs and other access methods

HTTP REST

JDBC

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

Java

Java
JavaScript
Objective-C

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

Java Stored Procedures

limited functionality with using 'rules'

yes

since Access 2010 using the ACE-engine

JavaScript

Triggers

yes

Callbacks are triggered when data changes

yes

since Access 2010 using the ACE-engine

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

but no files for transaction logging

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

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

Access rights can be defined down to the item level

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