DBMS > Google BigQuery vs. Microsoft Access vs. Oracle NoSQL vs. SiteWhere

System Properties Comparison Google BigQuery vs. Microsoft Access vs. Oracle NoSQL vs. SiteWhere

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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 multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Time Series 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	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

cloud.google.com/bigquery

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

www.oracle.com/database/nosql/technologies/nosql

github.com/sitewhere/sitewhere

Technical documentation

cloud.google.com/bigquery/docs

developer.microsoft.com/en-us/access

docs.oracle.com/en/database/other-databases/nosql-database/index.html

sitewhere1.sitewhere.io/index.html

Developer

Google

Microsoft

Oracle

SiteWhere

Initial release

2010

1992

2011

2010

Current release

1902 (16.0.11328.20222), March 2019

23.3, December 2023

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Common Public Attribution License Version 1.0

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++

Java

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

Linux
Solaris SPARC/x86

Linux
OS X
Windows

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

predefined scheme

Typing

predefined data types such as float or date

yes

optional

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

but not compliant to any SQL standard

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

ADO.NET
DAO
ODBC
OLE DB

RESTful HTTP API

HTTP REST

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

Triggers

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

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

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

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

off heap cache

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

Users with fine-grained authorization concept

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