DBMS > Microsoft Access vs. MongoDB vs. Oracle NoSQL vs. Snowflake vs. TypeDB

System Properties Comparison Microsoft Access vs. MongoDB vs. Oracle NoSQL vs. Snowflake vs. TypeDB

Editorial information provided by DB-Engines

Name

Description

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.)

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

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

Cloud-based data warehousing service for structured and semi-structured data

TypeDB is a strongly-typed database with a rich and logical type system and TypeQL as its query language

Primary database model

Relational DBMS

Document store

Document store
Key-value store
Relational DBMS

Relational DBMS

Graph DBMS
Relational DBMS

Often described as a 'hyper-relational' database, since it implements the 'Entity-Relationship Paradigm' to manage complex data structures and ontologies.

Secondary database models

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0
Vector DBMS

currently available in the MongoDB Atlas cloud service only

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

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	421.08
Rank	#5	Overall
	#1	Document stores

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.70
Rank	#230	Overall
	#20	Graph DBMS
	#106	Relational DBMS

Website

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

www.mongodb.com

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

www.snowflake.com

typedb.com

Technical documentation

developer.microsoft.com/en-us/access

www.mongodb.com/docs/manual

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

docs.snowflake.net/manuals/index.html

typedb.com/docs

Developer

Microsoft

MongoDB, Inc

Oracle

Snowflake Computing Inc.

Vaticle

Initial release

1992

2009

2011

2014

2016

Current release

1902 (16.0.11328.20222), March 2019

6.0.7, June 2023

24.1, May 2024

2.26.3, January 2024

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Open Source

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

commercial

Open Source

GPL Version 3, commercial licenses available

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Windows

Not a real database server, but making use of DLLs

Linux
OS X
Solaris
Windows

Linux
Solaris SPARC/x86

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

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

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

Read-only SQL queries via the MongoDB Atlas SQL Interface

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
DAO
ODBC
OLE DB

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

RESTful HTTP API

CLI Client
JDBC
ODBC

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (Visualisation software- previously TypeDB Workbase)

Supported programming languages

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

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Kotlin
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

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

JavaScript (Node.js)
Python

All JVM based languages
Groovy
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

JavaScript

user defined functions

Triggers

yes

since Access 2010 using the ACE-engine

yes

in MongoDB Atlas only

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

Sharding

yes

Sharding

by using Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

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

yes

Multi-source replication

by using Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

yes

by using Apache Kafka and Apache Zookeeper

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

typically not used, however similar functionality with DBRef possible

yes

substituted by the relationship feature

Transaction concepts

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

ACID

but no files for transaction logging

Multi-document ACID Transactions with snapshot isolation

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

optional, enabled by default

yes

In-memory capabilities

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

yes

In-memory storage engine introduced with MongoDB version 3.2

yes

off heap cache

User concepts

Access control

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

Access rights for users and roles

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

at REST API level; other APIs in progress

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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Apache f or language drivers, and AGPL and Commercial for the database server. The...
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