DBMS > Google BigQuery vs. Microsoft Access vs. Percona Server for MongoDB vs. RethinkDB

System Properties Comparison Google BigQuery vs. Microsoft Access vs. Percona Server for MongoDB vs. RethinkDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Relational DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	0.60
Rank	#246	Overall
	#39	Document stores

Score	2.66
Rank	#107	Overall
	#20	Document stores

Website

cloud.google.com/bigquery

www.microsoft.com/en-us/microsoft-365/access

www.percona.com/mongodb/software/percona-server-for-mongodb

rethinkdb.com

Technical documentation

cloud.google.com/bigquery/docs

developer.microsoft.com/en-us/access

docs.percona.com/percona-distribution-for-mongodb

rethinkdb.com/docs

Developer

Google

Microsoft

Percona

The Linux Foundation

since July 2017

Initial release

2010

1992

2015

2009

Current release

1902 (16.0.11328.20222), March 2019

3.4.10-2.10, November 2017

2.4.1, August 2020

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

Open Source

GPL Version 2

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

but not compliant to any SQL standard

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

proprietary protocol using JSON

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

JavaScript

Triggers

yes

since Access 2010 using the ACE-engine

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate 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

but no files for transaction logging

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

In-memory capabilities

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

yes

via In-Memory Engine

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

a simple user-level security was built in till version Access 2003

Access rights for users and roles

yes

users and table-level permissions

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