DBMS > atoti vs. MonetDB vs. Qdrant vs. Snowflake vs. Stardog

System Properties Comparison atoti vs. MonetDB vs. Qdrant vs. Snowflake vs. Stardog

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

A relational database management system that stores data in columns

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

Cloud-based data warehousing service for structured and semi-structured data

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

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

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

Website

atoti.io

www.monetdb.org

github.com/qdrant/qdrant
qdrant.tech

www.snowflake.com

www.stardog.com

Technical documentation

docs.atoti.io

www.monetdb.org/Documentation

qdrant.tech/documentation

docs.snowflake.net/manuals/index.html

docs.stardog.com

Developer

ActiveViam

MonetDB BV

Qdrant

Snowflake Computing Inc.

Stardog-Union

Initial release

2004

2021

2014

2010

Current release

Dec2023 (11.49), December 2023

7.3.0, May 2020

License

Commercial or Open Source

commercial

free versions available

Open Source

Mozilla Public License 2.0

Open Source

Apache Version 2.0

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

Java

Rust

Java

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS
Windows

hosted

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

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

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

Multidimensional Expressions (MDX)

yes

SQL 2003 with some extensions

yes

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

APIs and other access methods

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

CLI Client
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

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

JavaScript (Node.js)
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

Python

yes, in SQL, C, R

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

similar concept for controling cloud resources

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding via remote tables

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

Collection-level replication

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency, tunable 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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

Key-based authentication

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users and roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.