DBMS > Google BigQuery vs. MonetDB vs. Qdrant vs. Realm

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

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Editorial information provided by DB-Engines

Name

Description

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

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Relational DBMS

Vector DBMS

Document store

Secondary database models

Document store
Spatial DBMS

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

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Score	1.53
Rank	#145	Overall
	#7	Vector DBMS

Score	7.18
Rank	#52	Overall
	#8	Document stores

Website

cloud.google.com/bigquery

www.monetdb.org

github.com/qdrant/qdrant
qdrant.tech

realm.io

Technical documentation

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

qdrant.tech/documentation

realm.io/docs

Developer

Google

MonetDB BV

Qdrant

Realm, acquired by MongoDB in May 2019

Initial release

2010

2004

2021

2014

Current release

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

Apache Version 2.0

Open Source

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

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS
Windows

Android
Backend: server-less
iOS
Windows

Data scheme

yes

schema-free

yes

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.

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

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

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
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, in SQL, C, R

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

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

none

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

Foreign keys

Referential integrity

yes

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

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

In-Memory realm

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

yes

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