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Amazon RDS vs Amazon S3: What are the differences?

Amazon RDS (Relational Database Service) and Amazon S3 (Simple Storage Service) are two popular services offered by Amazon Web Services (AWS). Let's explore the key differences between them.

  1. Database vs. Storage: Amazon RDS is primarily a managed relational database service that allows users to set up, operate, and scale a relational database in the cloud. It provides fully-managed database instances for MySQL, PostgreSQL, Oracle, SQL Server, and Amazon Aurora. On the other hand, Amazon S3 is a scalable object storage service designed for storing and retrieving large amounts of unstructured data, such as images, videos, documents, backups, logs, etc.

  2. Data Structure: Amazon RDS deals with structured data and follows a fixed schema defined by the chosen database engine. It provides features like ACID (Atomicity, Consistency, Isolation, Durability) properties, data indexing, transactions, and SQL querying. In contrast, Amazon S3 does not impose any schema and allows storing any type of object in its buckets, making it versatile for storing files, objects, and media content of any format.

  3. Storage Management: Amazon RDS automatically manages database storage allocation, performance optimization, backups, and software patching. It provides features like automated backups, replication, and point-in-time recovery for data protection. On the other hand, Amazon S3 provides simple storage management, enabling users to create, delete, and organize buckets. It also offers various storage classes for cost optimization, data lifecycle policies, and server-side encryption for data security.

  4. Performance and Scalability: Amazon RDS allows users to choose the database instance type and provides options for read replicas and multi-AZ deployments to enhance performance and availability. It also supports auto-scaling based on workload demands. In contrast, Amazon S3 is designed for high-scalability, providing consistent performance and virtually unlimited storage capacity with automatic scaling. It is ideal for applications that require storing and retrieving large volumes of data with high throughput.

  5. Data Access and Integration: Amazon RDS allows direct access to the relational databases using standard database connectivity protocols like JDBC, ODBC, and network connectivity. It supports integrations with other AWS services like AWS Lambda, AWS Glue, and AWS Data Pipeline. On the other hand, Amazon S3 provides a RESTful API and SDKs for easy data access from applications. It integrates well with other AWS services like AWS Lambda, Amazon EMR, Amazon Athena, and Amazon Redshift for data processing and analytics.

  6. Cost Structure: Amazon RDS pricing is based on the database instance type, allocated storage, provisioned IOPS, backups, and data transfer. It offers multiple pricing options similar to EC2 instances. On the contrary, Amazon S3 pricing is based on the amount of storage used, data transfer, and requests made. It offers different storage tiers with varying costs based on data access patterns and availability requirements.

In summary, Amazon RDS is a managed relational database service for structured data, while Amazon S3 is an object storage service for storing unstructured data. RDS focuses on database management, performance, and structured querying, whereas S3 provides simple storage management, scalability, and versatility for storing large volumes of objects.

Decisions about Amazon RDS and Amazon S3
Gabriel Pa

We offer our customer HIPAA compliant storage. After analyzing the market, we decided to go with Google Storage. The Nodejs API is ok, still not ES6 and can be very confusing to use. For each new customer, we created a different bucket so they can have individual data and not have to worry about data loss. After 1000+ customers we started seeing many problems with the creation of new buckets, with saving or retrieving a new file. Many false positive: the Promise returned ok, but in reality, it failed.

That's why we switched to S3 that just works.

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Pros of Amazon RDS
Pros of Amazon S3
  • 165
    Reliable failovers
  • 156
    Automated backups
  • 130
    Backed by amazon
  • 92
    Db snapshots
  • 87
  • 30
    Control iops, fast restore to point of time
  • 28
  • 24
  • 20
    Push-button scaling
  • 20
    Automatic software patching
  • 4
  • 3
  • 2
  • 590
  • 492
  • 456
  • 329
    Simple & easy
  • 83
    Many sdks
  • 30
  • 13
    Easy Setup
  • 11
  • 11
    1000+ POPs
  • 6
  • 4
    Plug and play
  • 4
  • 3
    Web UI for uploading files
  • 2
    Faster on response
  • 2
  • 2
    GDPR ready
  • 1
    Easy to use
  • 1
  • 1
    Easy integration with CloudFront

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Cons of Amazon RDS
Cons of Amazon S3
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    • 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

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    What is Amazon RDS?

    Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.

    What is Amazon S3?

    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

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    What companies use Amazon RDS?
    What companies use Amazon S3?
    See which teams inside your own company are using Amazon RDS or Amazon S3.
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    What tools integrate with Amazon RDS?
    What tools integrate with Amazon S3?

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    What are some alternatives to Amazon RDS and Amazon S3?
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Apache Aurora
    Apache Aurora is a service scheduler that runs on top of Mesos, enabling you to run long-running services that take advantage of Mesos' scalability, fault-tolerance, and resource isolation.
    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.
    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
    Heroku Postgres
    Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.
    See all alternatives