Amazon S3 vs Atlas-DB vs MongoDB: What are the differences?
Differences between Amazon S3, Atlas-DB, and MongoDB
Introduction: In this article, we will explore and compare the key differences between Amazon S3, Atlas-DB, and MongoDB, highlighting their distinct features and functionalities.
Scalability and Performance:
Amazon S3 is a highly scalable object storage service that allows users to store and retrieve large amounts of data. It is designed to offer high durability and availability, making it suitable for storing static data files, backups, and logs. On the other hand, Atlas-DB is a globally distributed multi-model database service provided by MongoDB Atlas that offers high scalability, enabling businesses to handle growing workloads and serve concurrent requests efficiently. MongoDB, the underlying database technology, is a scalable NoSQL database known for its high performance and horizontal scalability, allowing for real-time data access and processing at scale.
Data Model:
Amazon S3 is an object storage service that stores data as objects within buckets. Each object consists of a unique key, data, and metadata. It provides a simple key-value API for storage and retrieval of objects. In contrast, Atlas-DB and MongoDB are document-oriented databases that store and retrieve data in JSON-like documents. This flexible schema allows for easy development and evolving data models as business requirements change.
Querying and Indexing:
While Amazon S3 provides a simple API for object storage, it does not offer built-in query capabilities. Users typically access the stored objects by specifying their unique keys. On the other hand, Atlas-DB and MongoDB provide powerful querying capabilities, allowing users to perform complex queries on the stored documents using a rich query language. They also support indexing, which helps improve query performance by creating indexes on specific fields.
Managed Service:
Amazon S3 is a managed storage service provided by Amazon Web Services (AWS). It takes care of the underlying infrastructure, ensuring durability, availability, and scalability. Similarly, Atlas-DB is a managed database service provided by MongoDB Atlas, offering fully managed deployments, automated backups, and scaling capabilities. MongoDB, being an open-source database, can be self-managed or managed through MongoDB Atlas. However, MongoDB Atlas provides additional features such as automated provisioning and monitoring.
Data Consistency and Durability:
Amazon S3 guarantees durability, meaning that objects stored in S3 are automatically replicated across multiple servers within a region to protect against data loss. However, it does not provide strong consistency guarantees and may exhibit eventual consistency. In contrast, Atlas-DB and MongoDB provide strong consistency for single-document operations by default. MongoDB's replication and sharding features ensure durability and high availability of the data.
Pricing Model:
Amazon S3 pricing is based on the amount of data stored, data transfer in and out, and the number of requests made. Atlas-DB pricing, being a managed database service, considers factors such as instance size, storage usage, and data transfer. MongoDB Community Edition is open source and free to use, while MongoDB Atlas pricing varies depending on the chosen tier and pricing model.
In summary, Amazon S3 is a scalable object storage service, while Atlas-DB and MongoDB are document-oriented databases that offer powerful querying capabilities and strong consistency. They differ in their data model, querying and indexing capabilities, managed service offerings, data consistency and durability, and pricing models.
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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.
Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly.
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.
