Amazon S3 vs MongoDB

Overview

Amazon S3

Stacks55.1K

Followers40.2K

Votes2.0K

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Amazon S3 vs MongoDB: What are the differences?

Amazon S3 is a scalable object storage service provided by Amazon Web Services (AWS). MongoDB, on the other hand, is a NoSQL database platform. Let's discuss the key differences between the two:

Scalability: Amazon S3 is a highly scalable storage service designed for handling large amounts of data, allowing users to store and retrieve any amount of data from anywhere. It offers near-infinite scalability, making it suitable for organizations with rapidly growing storage needs. On the other hand, MongoDB is a flexible and scalable NoSQL database that enables horizontal scaling by distributing data across multiple servers. It can handle massive amounts of data and support millions of transactions per second, making it ideal for high-performance applications.
Data Structure: Amazon S3 is an object storage service that stores data as objects within buckets. Each object is addressable using a unique key and can be up to 5 terabytes in size. Alternatively, MongoDB is a document-oriented database that stores data in flexible JSON-like documents called BSON. It allows for rich and dynamic schemas, making it easier to model complex data structures. Unlike Amazon S3, MongoDB supports the indexing and querying of data based on its structure, enabling more advanced data manipulation.
Querying and Indexing: While both Amazon S3 and MongoDB allow for data retrieval, MongoDB provides a more advanced querying and indexing functionality. MongoDB supports a powerful query language and allows for the creation of indexes on specific fields to improve the efficiency of data retrieval operations. In contrast, Amazon S3 is primarily designed for data storage and retrieval based on unique keys, without support for complex querying or indexing operations.
Data Consistency: Amazon S3 offers eventual consistency, meaning changes made to a stored object may take some time to propagate and become consistent across all regions and availability zones. This makes it suitable for applications where immediate consistency is not critical, such as storing static files. On the other hand, MongoDB provides strong consistency guarantees by default, ensuring that all replicas of a data set are consistent within a certain timeframe. This makes MongoDB a better choice for applications that require immediate, consistent access to data.
Data Processing and Analytics: While Amazon S3 primarily focuses on data storage, it integrates well with other AWS services, such as Amazon EMR (Elastic MapReduce) and Amazon Athena, to enable efficient data processing and analytics. These services allow users to run big data processing frameworks and perform complex analytics tasks directly on data stored in S3. In comparison, MongoDB provides built-in data processing capabilities through a powerful aggregation pipeline, allowing for real-time analytics and data transformations within the database itself.
Data Model Flexibility: MongoDB provides greater flexibility in terms of data modeling compared to Amazon S3. With MongoDB, users can store and access data in a more hierarchical and structured manner, allowing for more complex relationships between data elements. In contrast, Amazon S3 stores data as flat objects within buckets, without inherent support for hierarchical or relational connections between data.

In summary, Amazon S3 excels in scalable object storage and MongoDB provides a flexible and scalable NoSQL database solution with more advanced querying and indexing capabilities.

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Advice on Amazon S3, MongoDB

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

Amazon S3	MongoDB
Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web	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.
Write, read, and delete objects containing from 1 byte to 5 terabytes of data each. The number of objects you can store is unlimited.;Each object is stored in a bucket and retrieved via a unique, developer-assigned key.;A bucket can be stored in one of several Regions. You can choose a Region to optimize for latency, minimize costs, or address regulatory requirements. Amazon S3 is currently available in the US Standard, US West (Oregon), US West (Northern California), EU (Ireland), Asia Pacific (Singapore), Asia Pacific (Tokyo), Asia Pacific (Sydney), South America (Sao Paulo), and GovCloud (US) Regions. The US Standard Region automatically routes requests to facilities in Northern Virginia or the Pacific Northwest using network maps.;Objects stored in a Region never leave the Region unless you transfer them out. For example, objects stored in the EU (Ireland) Region never leave the EU.;Authentication mechanisms are provided to ensure that data is kept secure from unauthorized access. Objects can be made private or public, and rights can be granted to specific users.;Options for secure data upload/download and encryption of data at rest are provided for additional data protection.;Uses standards-based REST and SOAP interfaces designed to work with any Internet-development toolkit.;Built to be flexible so that protocol or functional layers can easily be added. The default download protocol is HTTP. A BitTorrent protocol interface is provided to lower costs for high-scale distribution.;Provides functionality to simplify manageability of data through its lifetime. Includes options for segregating data by buckets, monitoring and controlling spend, and automatically archiving data to even lower cost storage options. These options can be easily administered from the Amazon S3 Management Console.;Reliability backed with the Amazon S3 Service Level Agreement.	Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Statistics
GitHub Stars -	GitHub Stars 27.7K
GitHub Forks -	GitHub Forks 5.7K
Stacks 55.1K	Stacks 96.6K
Followers 40.2K	Followers 82.0K
Votes 2.0K	Votes 4.1K
Pros & Cons
Pros 590 Reliable 492 Scalable 456 Cheap 329 Simple & easy 83 Many sdks Cons 7 Permissions take some time to get right 6 Requires a credit card 6 Takes time/work to organize buckets & folders properly 3 Complex to set up	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

What are some alternatives to Amazon S3, MongoDB?

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.

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Amazon S3 vs MongoDB: What are the differences?

Amazon S3 is a scalable object storage service provided by Amazon Web Services (AWS). MongoDB, on the other hand, is a NoSQL database platform. Let's discuss the key differences between the two:

Scalability: Amazon S3 is a highly scalable storage service designed for handling large amounts of data, allowing users to store and retrieve any amount of data from anywhere. It offers near-infinite scalability, making it suitable for organizations with rapidly growing storage needs. On the other hand, MongoDB is a flexible and scalable NoSQL database that enables horizontal scaling by distributing data across multiple servers. It can handle massive amounts of data and support millions of transactions per second, making it ideal for high-performance applications.
Data Structure: Amazon S3 is an object storage service that stores data as objects within buckets. Each object is addressable using a unique key and can be up to 5 terabytes in size. Alternatively, MongoDB is a document-oriented database that stores data in flexible JSON-like documents called BSON. It allows for rich and dynamic schemas, making it easier to model complex data structures. Unlike Amazon S3, MongoDB supports the indexing and querying of data based on its structure, enabling more advanced data manipulation.
Querying and Indexing: While both Amazon S3 and MongoDB allow for data retrieval, MongoDB provides a more advanced querying and indexing functionality. MongoDB supports a powerful query language and allows for the creation of indexes on specific fields to improve the efficiency of data retrieval operations. In contrast, Amazon S3 is primarily designed for data storage and retrieval based on unique keys, without support for complex querying or indexing operations.
Data Consistency: Amazon S3 offers eventual consistency, meaning changes made to a stored object may take some time to propagate and become consistent across all regions and availability zones. This makes it suitable for applications where immediate consistency is not critical, such as storing static files. On the other hand, MongoDB provides strong consistency guarantees by default, ensuring that all replicas of a data set are consistent within a certain timeframe. This makes MongoDB a better choice for applications that require immediate, consistent access to data.
Data Processing and Analytics: While Amazon S3 primarily focuses on data storage, it integrates well with other AWS services, such as Amazon EMR (Elastic MapReduce) and Amazon Athena, to enable efficient data processing and analytics. These services allow users to run big data processing frameworks and perform complex analytics tasks directly on data stored in S3. In comparison, MongoDB provides built-in data processing capabilities through a powerful aggregation pipeline, allowing for real-time analytics and data transformations within the database itself.
Data Model Flexibility: MongoDB provides greater flexibility in terms of data modeling compared to Amazon S3. With MongoDB, users can store and access data in a more hierarchical and structured manner, allowing for more complex relationships between data elements. In contrast, Amazon S3 stores data as flat objects within buckets, without inherent support for hierarchical or relational connections between data.

In summary, Amazon S3 excels in scalable object storage and MongoDB provides a flexible and scalable NoSQL database solution with more advanced querying and indexing capabilities.