Amazon S3 vs MongoDB vs Sequelize: What are the differences?
Introduction:
When considering storage options for web applications, Amazon S3, MongoDB, and Sequelize are popular choices. Each has its own set of features and use cases. Below are key differences between Amazon S3 and MongoDB along with Sequelize.
Data Structure: Amazon S3 is an object storage service that stores data as objects, whereas MongoDB is a NoSQL database that stores data in the form of documents organized in collections. On the other hand, Sequelize is an ORM (Object-Relational Mapping) that facilitates interaction with SQL databases. Each data structure has its own advantages and suitability for different types of applications.
Scalability: Amazon S3 is highly scalable, capable of storing and retrieving a vast amount of data with ease, making it suitable for large-scale applications. MongoDB is also scalable, allowing for horizontal scaling by distributing data across multiple servers. Sequelize, being an ORM, integrates with relational databases, which can offer scalability but may require additional effort to configure.
Query Language: Amazon S3 does not support query language directly as it is primarily used for storage and retrieval of objects. MongoDB, on the other hand, supports a rich query language that allows for complex queries and data manipulation. Sequelize provides query capabilities for relational databases using SQL, offering powerful querying capabilities for structured data.
Data Consistency: Amazon S3 provides eventual consistency for object updates, meaning changes may take time to propagate across all regions. MongoDB offers strong consistency by default, ensuring that data is always up to date within a cluster. Sequelize adheres to the consistency models of the underlying relational database system, providing strong consistency for SQL databases.
Indexing and Performance: Amazon S3 does not support indexing or querying directly on the stored objects, making it less suitable for scenarios requiring complex data retrieval. MongoDB supports indexing, which improves query performance by allowing for faster data access. Sequelize integrates with relational databases that support indexing and query optimization, enhancing performance for SQL-based applications.
Data Relationships: Amazon S3 does not inherently support relationships between objects stored in buckets, requiring developers to manage relationships manually if needed. MongoDB allows for nested documents and references to establish relationships between data, providing flexibility in data modeling. Sequelize, being an ORM, facilitates the definition of relationships between data entities in a relational database, simplifying data management and retrieval.
In Summary, Amazon S3 is ideal for scalable object storage, MongoDB excels in document-based NoSQL data management, and Sequelize simplifies database interactions through ORM for relational databases. Each has its own strengths and is suitable for different use cases in web application development.
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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!
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.
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.
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.
Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL,
MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and
more.
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.
