DBMS > Google BigQuery vs. Kinetica vs. MonetDB vs. VoltDB

System Properties Comparison Google BigQuery vs. Kinetica vs. MonetDB vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

A relational database management system that stores data in columns

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

cloud.google.com/bigquery

www.kinetica.com

www.monetdb.org

www.voltdb.com

Technical documentation

cloud.google.com/bigquery/docs

docs.kinetica.com

www.monetdb.org/Documentation

docs.voltdb.com

Developer

Google

Kinetica

MonetDB BV

VoltDB Inc.

Initial release

2010

2012

2004

2010

Current release

7.1, August 2021

Dec2023 (11.49), December 2023

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C, C++

Java, C++

Server operating systems

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

for development

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

SQL 2003 with some extensions

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

yes, in SQL, C, R

Java

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication available in experimental status

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

GPU vRAM or System RAM

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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