DBMS > FatDB vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. MySQL

System Properties Comparison FatDB vs. JaguarDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. MySQL

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

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

Performant, highly scalable DBMS for AI and IoT applications

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used open source RDBMS

Primary database model

Document store
Key-value store

Key-value store
Vector DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

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

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

www.jaguardb.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

www.mysql.com

Technical documentation

www.jaguardb.com/support.html

learn.microsoft.com/azure/cosmos-db

dev.mysql.com/doc

Developer

FatCloud

DataJaguar, Inc.

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2012

2015

2014

2012

1995

Current release

3.3 July 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

the server part. Clients available in other languages

C and C++

Server operating systems

Windows

Linux

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

Via inetgration in SQL Server

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

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

JDBC
ODBC

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

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

via applications

JavaScript

yes

proprietary syntax

Triggers

yes

via applications

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

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

Can implement custom security layer via applications

rights management via user accounts

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept

no user groups or roles

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