DBMS > Cachelot.io vs. Google BigQuery vs. LokiJS vs. Microsoft Access vs. Oracle NoSQL

System Properties Comparison Cachelot.io vs. Google BigQuery vs. LokiJS vs. Microsoft Access vs. Oracle NoSQL

Editorial information provided by DB-Engines

Name

Description

In-memory caching system

Large scale data warehouse service with append-only tables

In-memory JavaScript DBMS

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

Primary database model

Document store
Key-value store
Relational DBMS

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Trend Chart

Score	0.00
Rank	#396	Overall
	#64	Key-value stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.48
Rank	#262	Overall
	#42	Document stores

Score	105.40
Rank	#11	Overall
	#8	Relational DBMS

Score	2.96
Rank	#103	Overall
	#18	Document stores
	#17	Key-value stores
	#52	Relational DBMS

Website

cachelot.io

cloud.google.com/bigquery

github.com/techfort/LokiJS

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

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

Technical documentation

cloud.google.com/bigquery/docs

techfort.github.io/LokiJS

developer.microsoft.com/en-us/access

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

Developer

Google

Microsoft

Oracle

Initial release

2015

2010

2014

1992

2011

Current release

1902 (16.0.11328.20222), March 2019

23.3, December 2023

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

commercial

Bundled with Microsoft Office

Open Source

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

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++

JavaScript

C++

Java

Server operating systems

FreeBSD
Linux
OS X

hosted

server-less, requires a JavaScript environment (browser, Node.js)

Windows

Not a real database server, but making use of DLLs

Linux
Solaris SPARC/x86

Data scheme

schema-free

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

optional

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

via views

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

SQL-like DML and DDL statements

APIs and other access methods

Memcached protocol

RESTful HTTP/JSON API

JavaScript API

ADO.NET
DAO
ODBC
OLE DB

RESTful HTTP API

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

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

View 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

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

none

Eventual Consistency
Immediate Consistency

depending on configuration

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

atomic operations within a single collection possible

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

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

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

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