DBMS > Databricks vs. Google BigQuery vs. MonetDB

System Properties Comparison Databricks vs. Google BigQuery vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Large scale data warehouse service with append-only tables

A relational database management system that stores data in columns

Primary database model

Document store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	76.33
Rank	#17	Overall
	#3	Document stores
	#11	Relational DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#148	Overall
	#68	Relational DBMS

Website

www.databricks.com

cloud.google.com/bigquery

www.monetdb.org

Technical documentation

docs.databricks.com

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

Developer

Databricks

Google

MonetDB BV

Initial release

2013

2010

2004

Current release

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

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

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

SQL 2003 with some extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

Python
R
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in JavaScript

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

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.

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

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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