Microsoft Access vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Microsoft Access

Stacks83

Followers87

Votes0

Microsoft Access vs MongoDB: What are the differences?

1. Storage Structure: Microsoft Access is a relational database management system (RDBMS) that stores data in tables with predefined schemas and relationships. On the other hand, MongoDB is a NoSQL database which stores data in flexible, JSON-like documents, allowing for dynamic and unstructured schemas.
2. Scalability: While Microsoft Access is designed for small to medium-sized applications and has limitations on scalability, MongoDB is designed to scale horizontally by distributing data across multiple servers, making it suitable for handling large amounts of data and high traffic loads.
3. Query Language: Microsoft Access uses SQL (Structured Query Language) as a standard query language for retrieving and manipulating data. MongoDB, on the other hand, uses a comprehensive query language called MongoDB Query Language (MQL) which includes additional features like geospatial queries and aggregation.
4. Data Replication and High Availability: Microsoft Access does not have built-in features for data replication or high availability. MongoDB, however, offers replication features that provide automatic failover and data redundancy, ensuring data availability and reliability.
5. Data Model: Microsoft Access uses a traditional relational model, with tables, rows, and columns, enforcing structured relationships between entities. MongoDB, being a document-oriented database, allows for flexible and dynamic schemas, making it easier to handle semi-structured and unstructured data.
6. Deployment and Hosting: Microsoft Access is typically deployed on a single physical or virtual machine, limiting its flexibility in terms of deployment options. MongoDB, in contrast, supports various deployment options including on-premise, cloud-based, and hybrid deployments, offering more flexibility and scalability.

In Summary, Microsoft Access is a relational database management system with predefined schemas, while MongoDB is a NoSQL database with flexible document-based schemas. MongoDB provides scalability, a comprehensive query language, data replication, and high availability features, supports flexible data modeling, and offers more deployment options compared to Microsoft Access.

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Advice on MongoDB, Microsoft Access

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

MongoDB	Microsoft Access
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.	It is an easy-to-use tool for creating business applications, from templates or from scratch. With its rich and intuitive design tools, it can help you create appealing and highly functional applications in a minimal amount of time.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	rich and intuitive design tools; highly functional applications in a minimal amount of time
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 83
Followers 82.0K	Followers 87
Votes 4.1K	Votes 0
Pros & Cons
Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language	No community feedback yet
Integrations
No integrations available	Microsoft SQL Server Azure SQL Database

What are some alternatives to MongoDB, Microsoft Access?

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Microsoft Access vs MongoDB: What are the differences?

1. Storage Structure: Microsoft Access is a relational database management system (RDBMS) that stores data in tables with predefined schemas and relationships. On the other hand, MongoDB is a NoSQL database which stores data in flexible, JSON-like documents, allowing for dynamic and unstructured schemas.
2. Scalability: While Microsoft Access is designed for small to medium-sized applications and has limitations on scalability, MongoDB is designed to scale horizontally by distributing data across multiple servers, making it suitable for handling large amounts of data and high traffic loads.
3. Query Language: Microsoft Access uses SQL (Structured Query Language) as a standard query language for retrieving and manipulating data. MongoDB, on the other hand, uses a comprehensive query language called MongoDB Query Language (MQL) which includes additional features like geospatial queries and aggregation.
4. Data Replication and High Availability: Microsoft Access does not have built-in features for data replication or high availability. MongoDB, however, offers replication features that provide automatic failover and data redundancy, ensuring data availability and reliability.
5. Data Model: Microsoft Access uses a traditional relational model, with tables, rows, and columns, enforcing structured relationships between entities. MongoDB, being a document-oriented database, allows for flexible and dynamic schemas, making it easier to handle semi-structured and unstructured data.
6. Deployment and Hosting: Microsoft Access is typically deployed on a single physical or virtual machine, limiting its flexibility in terms of deployment options. MongoDB, in contrast, supports various deployment options including on-premise, cloud-based, and hybrid deployments, offering more flexibility and scalability.

Microsoft Access vs MongoDB

Overview