DBMS > Google BigQuery vs. Microsoft Access vs. Microsoft Azure SQL Database vs. MySQL vs. PlanetScale

System Properties Comparison Google BigQuery vs. Microsoft Access vs. Microsoft Azure SQL Database vs. MySQL vs. PlanetScale

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.)

Database as a Service offering with high compatibility to Microsoft SQL Server

Widely used open source RDBMS

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Primary database model

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Website

cloud.google.com/bigquery

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

azure.microsoft.com/en-us/products/azure-sql/database

www.mysql.com

planetscale.com

Technical documentation

cloud.google.com/bigquery/docs

developer.microsoft.com/en-us/access

docs.microsoft.com/en-us/azure/azure-sql

dev.mysql.com/doc

planetscale.com/docs

Developer

Google

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

PlanetScale

Initial release

2010

1992

2010

1995

2020

Current release

1902 (16.0.11328.20222), March 2019

V12

8.4.0, April 2024

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C++

C and C++

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

yes

with proprietary extensions

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

Transact SQL

yes

proprietary syntax

yes

proprietary syntax

Triggers

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, with always 3 replicas available

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

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

not for MyISAM storage engine

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

ACID

not for MyISAM storage engine

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

table locks or row locks depending on storage engine

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

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept

no user groups or roles

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