DBMS > atoti vs. Google BigQuery vs. MonetDB vs. Qdrant vs. Stardog

System Properties Comparison atoti vs. Google BigQuery vs. MonetDB vs. Qdrant 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.

Large scale data warehouse service with append-only tables

A relational database management system that stores data in columns

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

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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

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

Website

atoti.io

cloud.google.com/bigquery

www.monetdb.org

github.com/qdrant/qdrant
qdrant.tech

www.stardog.com

Technical documentation

docs.atoti.io

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

qdrant.tech/documentation

docs.stardog.com

Developer

ActiveViam

Google

MonetDB BV

Qdrant

Stardog-Union

Initial release

2010

2004

2021

2010

Current release

Dec2023 (11.49), December 2023

7.3.0, May 2020

License

Commercial or Open Source

commercial

free versions available

commercial

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

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Implementation language

Java

Rust

Java

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

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.

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

APIs and other access methods

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

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
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

Python

user defined functions

in JavaScript

yes, in SQL, C, R

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Sharding via remote tables

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

Collection-level replication

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

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

Since BigQuery is designed for querying 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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Key-based authentication

Access rights for users and roles

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