Amazon S3 vs Azure Storage vs MongoDB

Overview

Amazon S3

Stacks55.1K

Followers40.2K

Votes2.0K

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Azure Storage

Stacks1.3K

Followers787

Votes52

Amazon S3 vs Azure Storage vs MongoDB: What are the differences?

Introduction

Amazon S3, Azure Storage, and MongoDB are three popular cloud-based storage solutions, each with its unique features and use cases. Understanding the key differences between them can help organizations make informed decisions when choosing a storage solution that best fits their needs.

1. Scalability and Availability:

Amazon S3 and Azure Storage both provide high scalability and availability. However, Amazon S3 offers "11 nines" (99.999999999%) durability, ensuring that data is highly resilient. On the other hand, Azure Storage provides "4 nines" (99.99%) durability. MongoDB, being a NoSQL database, also offers high scalability and availability.

2. Data Structure:

Amazon S3 and Azure Storage are object-based storage solutions, which means they store data as objects with unique identifiers. However, MongoDB is a document-based NoSQL database, where data is stored as documents in a JSON-like format. This allows for more flexible and complex data structures, suitable for applications that require rich and dynamic data models.

3. Querying and Indexing:

In terms of querying and indexing capabilities, MongoDB excels. It provides a powerful query language and supports indexing for efficient data retrieval. Moreover, MongoDB allows ad-hoc queries on unstructured data, making it suitable for applications that require real-time analytics and complex querying. Amazon S3 and Azure Storage, being object storages, do not offer the same level of querying and indexing capabilities as MongoDB.

4. Data Consistency and Transactions:

When it comes to data consistency and support for transactions, MongoDB provides strong ACID (Atomicity, Consistency, Isolation, Durability) guarantees. It ensures that data remains consistent even in the presence of concurrent updates. In contrast, Amazon S3 and Azure Storage do not offer strong consistency or built-in transaction support. While consistency can be achieved through additional application logic, MongoDB simplifies this by providing built-in transaction support.

5. Pricing Model:

Amazon S3, Azure Storage, and MongoDB have different pricing models. Amazon S3 and Azure Storage follow a pay-as-you-go model, where users pay for the storage used and data transfer. In contrast, MongoDB offers a subscription-based pricing model, where users pay a fixed fee based on the capacity and performance required. This makes MongoDB a better option for organizations with predictable workloads and budget constraints.

6. Integration and Ecosystem:

Amazon S3 and Azure Storage have extensive integration and ecosystem support. They integrate seamlessly with other cloud services, such as compute instances and content delivery networks. Additionally, they have a wide range of SDKs and APIs available for different programming languages, making it easier to develop applications. MongoDB also offers integration options and SDKs, but its ecosystem may not be as mature as Amazon S3 or Azure Storage.

In Summary, Amazon S3 and Azure Storage are object-based storage solutions, providing high scalability and availability. However, they differ in terms of data structure, querying capabilities, data consistency, pricing models, and integration options. MongoDB, as a NoSQL database, offers document-based storage, powerful querying, strong consistency, subscription-based pricing, and a growing ecosystem.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Amazon S3, MongoDB, Azure Storage

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	Azure Storage
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.	Azure Storage provides the flexibility to store and retrieve large amounts of unstructured data, such as documents and media files with Azure Blobs; structured nosql based data with Azure Tables; reliable messages with Azure Queues, and use SMB based Azure Files for migrating on-premises applications to the cloud.
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	Blobs, Tables, Queues, and Files;Highly scalable;Durable & highly available;Premium Storage;Designed for developers
Statistics
GitHub Stars -	GitHub Stars 27.7K	GitHub Stars -
GitHub Forks -	GitHub Forks 5.7K	GitHub Forks -
Stacks 55.1K	Stacks 96.6K	Stacks 1.3K
Followers 40.2K	Followers 82.0K	Followers 787
Votes 2.0K	Votes 4.1K	Votes 52
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	Pros 24 All-in-one storage solution 15 Pay only for data used regardless of disk size 9 Shared drive mapping 2 Cheapest hot and cloud storage 2 Cost-effective Cons 2 Direct support is not provided by Azure storage
Integrations
No integrations available	No integrations available	Microsoft Azure

What are some alternatives to Amazon S3, MongoDB, Azure Storage?

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

Amazon S3 vs Azure Storage vs MongoDB

Overview

Amazon S3 vs Azure Storage vs MongoDB: What are the differences?

Introduction

1. Scalability and Availability:

2. Data Structure:

3. Querying and Indexing:

4. Data Consistency and Transactions:

5. Pricing Model: