DBMS > FatDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. SQream DB vs. Stardog

System Properties Comparison FatDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. SQream DB vs. Stardog

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.

Globally distributed, horizontally scalable, multi-model database service

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

a GPU-based, columnar RDBMS for big data analytics workloads

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store
Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Spatial DBMS

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

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	0.70
Rank	#227	Overall
	#104	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

azure.microsoft.com/services/cosmos-db

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

sqream.com

www.stardog.com

Technical documentation

learn.microsoft.com/azure/cosmos-db

docs.sqream.com

docs.stardog.com

Developer

FatCloud

Microsoft

SQream Technologies

Stardog-Union

Initial release

2012

2014

2012

2017

2010

Current release

2022.1.6, December 2022

7.3.0, May 2020

License

Commercial or Open Source

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++, CUDA, Haskell, Java, Scala

Java

Server operating systems

Windows

hosted

Linux

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

JSON types

yes

yes, ANSI Standard SQL 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.

Import/export of XML data possible

Secondary indexes

yes

All properties auto-indexed by default

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like query language

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

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

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

RESTful HTTP API

.Net
JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

.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

C++
Java
JavaScript (Node.js)
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

via applications

JavaScript

user defined functions in Python

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via applications

JavaScript

yes

via event handlers

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 and vertical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

yes

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

none

Multi-source replication in HA-Cluster

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

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

Concurrency

Support for concurrent manipulation of data

yes

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

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

Access rights for users and roles

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