DBMS > FatDB vs. Microsoft Azure AI Search vs. Qdrant vs. SQLite vs. Stardog

System Properties Comparison FatDB vs. Microsoft Azure AI Search vs. Qdrant vs. SQLite 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.

Search-as-a-service for web and mobile app development

A high-performance vector database with neural network or semantic-based matching

Widely used embeddable, in-process RDBMS

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

Primary database model

Document store
Key-value store

Secondary database models

Vector DBMS

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

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

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

Website

azure.microsoft.com/en-us/services/search

github.com/qdrant/qdrant
qdrant.tech

www.sqlite.org

www.stardog.com

Technical documentation

learn.microsoft.com/en-us/azure/search

qdrant.tech/documentation

www.sqlite.org/docs.html

docs.stardog.com

Developer

FatCloud

Microsoft

Qdrant

Dwayne Richard Hipp

Stardog-Union

Initial release

2012

2015

2021

2000

2010

Current release

3.45.3 (15 April 2024), April 2024

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Public Domain

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Rust

Java

Server operating systems

Windows

hosted

Docker
Linux
macOS
Windows

server-less

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

yes

not rigid because of 'dynamic typing' concept.

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

Keywords, numberic ranges, geo, full-text

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

Via inetgration in SQL Server

yes

SQL-92 is not fully supported

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

RESTful HTTP API

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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

C#
Java
JavaScript
Python

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

via applications

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via applications

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

Collection-level replication

none

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency, tunable 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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

yes

using Azure authentication

Key-based authentication

Access rights for users and roles

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