DBMS > Derby vs. FatDB vs. Microsoft Azure Cosmos DB vs. Newts vs. Quasardb

System Properties Comparison Derby vs. FatDB vs. Microsoft Azure Cosmos DB vs. Newts vs. Quasardb

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Globally distributed, horizontally scalable, multi-model database service

Time Series DBMS based on Cassandra

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Document store
Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

db.apache.org/derby

azure.microsoft.com/services/cosmos-db

opennms.github.io/newts

quasar.ai

Technical documentation

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

learn.microsoft.com/azure/cosmos-db

github.com/OpenNMS/newts/wiki

doc.quasar.ai/master

Developer

Apache Software Foundation

FatCloud

Microsoft

OpenNMS Group

quasardb

Initial release

1997

2012

2014

2009

Current release

10.17.1.0, November 2023

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

commercial

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

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

All OS with a Java VM

Windows

hosted

Linux
OS X
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

integer and binary

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

yes

with tags

SQL

Support of SQL

yes

Via inetgration in SQL Server

SQL-like query language

APIs and other access methods

JDBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

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

HTTP REST
Java API

HTTP API

Supported programming languages

Java

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

Java

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes

via applications

JavaScript

Triggers

yes

via applications

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

Implicit feature of the cloud service

selectable replication factor

based on Cassandra

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Can implement custom security layer via applications

Access rights can be defined down to the item level

Cryptographically strong user authentication and audit trail

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