DBMS > Google BigQuery vs. MonetDB vs. Spark SQL vs. SwayDB

System Properties Comparison Google BigQuery vs. MonetDB vs. Spark SQL vs. SwayDB

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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

Spark SQL is a component on top of 'Spark Core' for structured data processing

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#148	Overall
	#68	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#373	Overall
	#57	Key-value stores

Website

cloud.google.com/bigquery

www.monetdb.org

spark.apache.org/sql

swaydb.simer.au

Technical documentation

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Google

MonetDB BV

Apache Software Foundation

Simer Plaha

Initial release

2010

2004

2014

2018

Current release

Dec2023 (11.49), December 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

Apache 2.0

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java
Python
R
Scala

Java
Kotlin
Scala

Server-side scripts

Stored procedures

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

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

none

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

Since BigQuery is designed for querying data

ACID

Atomic execution of operations

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

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

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