What is Amazon S3? Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. Customers of all sizes and industries can use Amazon S3 to store and protect any amount of data for a range of use cases, such as data lakes, websites, mobile applications, backup and restore, archive, enterprise applications, IoT devices, and big data analytics. Amazon S3 provides management features so that you can optimize, organize, and configure access to your data to meet your specific business, organizational, and compliance requirements.
What is Amazon S3?
Amazon S3 is a secure and redundant storage system that uses a flat object storage structure.
Data is stored in three different physical availability zone that also provides redundancy.
Different storage classes options are offered, balacing cost and availability. Automated management of storage can move objects to cheaper storage classes if they are accessed infrequently. Objects can also be automatically expire and be deleted after a specified time.
Access can be managed by access control lists or AWS Identity and Access Management (IAM).
Server access requests to buckets can be logged for security and analytics. AWS CloudTrail also enables logging at object level.
Amazon S3 can also be used to host static websites and client-side scripts.
Amazon S3 data consistency model
Amazon S3 provides strong read-after-write consistency for PUT and DELETE requests of objects in your Amazon S3 bucket in all AWS Regions. This behavior applies to both writes to new objects as well as PUT requests that overwrite existing objects and DELETE requests. In addition, read operations on Amazon S3 Select, Amazon S3 access controls lists (ACLs), Amazon S3 Object Tags, and object metadata (for example, the HEAD object) are strongly consistent.
Updates to a single key are atomic. For example, if you make a PUT request to an existing key from one thread and perform a GET request on the same key from a second thread concurrently, you will get either the old data or the new data, but never partial or corrupt data.
Amazon S3 achieves high availability by replicating data across multiple servers within AWS data centers. If a PUT request is successful, your data is safely stored. Any read (GET or LIST request) that is initiated following the receipt of a successful PUT response will return the data written by the PUT request. Here are examples of this behavior:
- A process writes a new object to Amazon S3 and immediately lists keys within its bucket. The new object appears in the list.
- A process replaces an existing object and immediately tries to read it. Amazon S3 returns the new data.
- A process deletes an existing object and immediately tries to read it. Amazon S3 does not return any data because the object has been deleted.
- A process deletes an existing object and immediately lists keys within its bucket. The object does not appear in the listing.
Bucket configurations have an eventual consistency model. Specifically, this means that:
- If you delete a bucket and immediately list all buckets, the deleted bucket might still appear in the list.
- If you enable versioning on a bucket for the first time, it might take a short amount of time for the change to be fully propagated. We recommend that you wait for 15 minutes after enabling versioning before issuing write operations (PUT or DELETE requests) on objects in the bucket.
What is AWS S3: Amazon S3 Storage Classes
Let’s have a look at the different storage classes using the example of a school:
- Amazon S3 Standard for frequent data access: Suitable for a use case where the latency should below. Example: Frequently accessed data will be the data of students’ attendance, which should be retrieved quickly.
- Amazon S3 Standard for infrequent data access: Can be used where the data is long-lived and less frequently accessed. Example: Students’ academic records will not be needed daily, but if they have any requirement, their details should be retrieved quickly.
- Amazon Glacier: Can be used where the data has to be archived, and high performance is not required. Example: Ex-student’s old record (like admission fee) will not be needed daily, and even if it is necessary, low latency is not required.
- One Zone-IA Storage Class: It can be used where the data is infrequently accessed and stored in a single region. Example: Student’s report card is not used daily and stored in a single availability region (i.e., school).
- Amazon S3 Standard Reduced Redundancy storage: Suitable for a use case where the data is non-critical and reproduced quickly. Example: Books in the library are non-critical data and can be replaced if lost.
Why Should I Consider Using Amazon S3?
Now that we’ve answered the question “What is AWS S3?” let’s dig into why you should consider using it. There are two big reasons why you should use Amazon S3, and they’re points we’ve covered before but bear calling further attention to:
- S3 offers 99.999999999% (11 9s) data durability. That means, if you stored 10,000,000 objects in Amazon S3, you would only lose a single object every 10,000 years. That is durable!
- S3 will automatically create and store copies of every uploaded object across many systems. This protects your data against errors, failures, and threats while guaranteeing you complete data availability when you need it.
Also, it doesn’t hurt that Amazon Web Services is the most popular cloud provider available today.
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We’ve covered cloud storage, the types of storage offered by AWS, how storage worked before AWS S3, an overview of AWS S3, objects and buckets, storage classes, and the various features of S3.
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