Client Amazon S3 Features and Technical Details

Amazon S3 is a highly durable and available object store that can store and serve large amounts of data.

Objects in S3 can range in size from 0 bytes to 5 terabytes, making it suitable for storing large files such as videos and images.

S3 supports both IPv4 and IPv6, allowing for connectivity from a wide range of devices and networks.

Objects in S3 are stored in buckets, which are essentially containers that hold a large number of objects.

Each object in S3 has a unique identifier, known as a key, that is used to access and retrieve the object.

S3 supports server-side encryption, which allows you to encrypt objects as they are being uploaded to S3.

S3 also supports versioning, which allows you to keep multiple versions of an object in your bucket.

The Amazon S3 API is a RESTful API that allows you to interact with S3 programmatically.

S3 also supports a number of other features, including cross-region replication, which allows you to replicate objects across different regions.

To get started with Amazon S3, you need to create a bucket. Each file and folder in S3 is contained in a bucket, which is like a top-level folder or drive.

Bucket names are globally unique, so make sure to choose a unique name for your bucket. Try to choose a bucket name that's a valid host name and everything should be in lower case if you plan to use it for web hosting.

In CrossFTP, you can create a bucket by going to the root folder and choosing New -> Bucket from the popup context menu, or by pressing the new Folder button.

When creating a bucket, you'll be allowed to choose where your bucket's region is. This is an important step, as it will determine how your data is stored and accessed.

Storage and Security is a top priority when using Client Amazon S3. Server Side Encryption can be enabled for all uploads by navigating to Sites -> Site Manager -> S3 -> Enable server side encryption.

This adds an extra layer of protection to your files, ensuring they remain secure even in the event of a data breach. You can also enable Client Side Encryption for all uploads by going to Sites -> Site Manager -> Security -> Local Encryption for File Transfer -> Enable encryption.

For more details on Client Side Encryption, you can check out the page dedicated to it.

Amazon S3 offers a range of storage classes to suit different needs and budgets. You can reduce costs by storing non-critical, reproducible data at lower levels of redundancy than standard storage using Reduced Redundancy Storage (RRS).

To access the storage class for existing files, right-click on the file pane's column head and toggle Storage Class from the popup menu. This will display the current storage class for the selected files.

To set up RRS for new files, toggle Sites -> Site Manager -> S3 -> Reduced Redundancy Storage (RRS). All new uploaded files will then be stored in the RRS class. For existing files, you can choose Properties... -> Metadata and add a new key-value pair with x-amz-storage-class as the key and REDUCED_REDUNDANCY as the value.

Amazon S3 has nine different storage classes, each with varying levels of durability, availability, and performance requirements. The default storage class is Amazon S3 Standard, which is suitable for frequently accessed data.

Here are the nine storage classes offered by Amazon S3:

If you want to archive an object to Amazon Glacier, you can define a Lifecycle Rule. The transited objects will be visible in S3 with storage class GLACIER.

Encryption is a crucial aspect of storage and security. Server Side Encryption can be enabled for all uploads by navigating to Sites -> Site Manager -> S3 -> Enable server side encryption.

To ensure your files are protected, you can also enable Client Side Encryption for all uploads by going to Sites -> Site Manager -> Security -> Local Encryption for File Transfer -> Enable encryption.

For more details on Client Side Encryption, check out the specific page that explains it further.

Amazon S3's Service Level Agreement (SLA) has some limitations that customers should be aware of. It primarily addresses data loss from hardware failures, but doesn't cover losses from human errors, misconfigurations, or security breaches.

Customers are responsible for monitoring SLA compliance and submitting claims within a designated timeframe. This can be a significant burden.

The SLA percentages and conditions can vary from those of other AWS services. This means customers need to understand how deviations from SLAs are calculated.

In cases of data loss due to hardware failure, Amazon doesn't provide monetary compensation. Instead, affected users may receive credits if they meet the eligibility criteria.

File size limits are an essential consideration when working with cloud storage, and Amazon S3 has some specific rules to keep in mind.

An object in S3 can be between 0 bytes and 5TB, which is a pretty wide range. However, if an object is larger than 5TB, it needs to be divided into chunks prior to uploading.

Objects larger than 5GB can't be uploaded in one go, so they must be uploaded via the S3 multipart upload API. This allows for more flexibility and control when uploading large files.

You can upload data to Amazon S3 from your local machine using the AWS Command Line Interface tool. This tool supports commands specific to AWS in your MATLAB command window.

To upload data, create a bucket for your data using the command `!aws s3 mb s3://MyCloudData`. Then, upload your data using the command `!aws s3 cp mylocaldatapath s3://MyCloudData --recursive`.

You can also use datastores to access data from Amazon S3. For example, use an `imageDatastore` object to read images from an Amazon S3 bucket. Create the datastore object with the URL of the bucket, and then read the images using the `readimage` function.

Here's a list of steps to upload and access data:

You can use datastores to access large data sets in Amazon S3 from your MATLAB client or cluster workers. A datastore is a repository for collections of data that are too large to fit in memory.

To read data from Amazon S3 using datastores, you can create an imageDatastore object that points to the URL of the Amazon S3 bucket. For example, you can use the imageDatastore function to read images from an Amazon S3 bucket. Replace s3://MyCloudData with the URL of your Amazon S3 bucket.

Here's an example of how to create an imageDatastore object: imds = imageDatastore("s3://MyCloudData/FoodImageDataset/", IncludeSubfolders=true, LabelSource="foldernames").

You can then read the first image from Amazon S3 using the readimage function: img = readimage(imds,1).

Display the image using the imshow function: imshow(img).

Datastores allow you to read and process data stored in multiple files on a remote location as a single entity. This makes it easier to work with large data sets in Amazon S3.

Here's a summary of the steps to read data from Amazon S3 using datastores:

By using datastores, you can easily access and process large data sets in Amazon S3 from your MATLAB client or cluster workers.

Urls are a crucial part of uploading and accessing data. You can generate them by right-clicking on objects and choosing URL... from the context menu.

To create a normal URL or path, simply select the object and generate the URL. It's that easy.

You can also generate BitTorrent URLs by toggling the "Generate BitTorrent URL" option on the URL dialog. This is useful for sharing large files.

Signed URLs can be generated by toggling the "Sign URL with expiration date" option on the URL dialog. This adds an extra layer of security to your URL.

If you need to share data privately, you can use Signed URLs for Private Distribution. To do this, choose the Private Distribution from CNAME option on the URL dialog, configure the signing policy, and then sign the URL with an expiration date.

Here are the different types of URLs you can generate:

Uploading data is a crucial step in working with data, and there are several ways to do it. You can upload data to Amazon S3 from your local machine using the AWS Command Line Interface tool.

To download data sets to your local machine, you can use commands in MATLAB, such as downloading the Example Food Images data set using `matlab.internal.examples.downloadSupportFile`. Once you have the data sets on your local machine, you can upload them to Amazon S3 using the `aws s3 cp` command.

For example, to upload the Example Food Images data set from your local machine to your Amazon S3 bucket, use the command `!aws s3 cp MyLocalFolder/FoodImageDataset s3://MyCloudData/FoodImageDataset/ --recursive`. This command copies the data from your local machine to your Amazon S3 bucket, including all subfolders and files.

You can also use the AWS S3 web page to upload data, but using the command line can be more efficient.

Here are the steps to upload data to Amazon S3 using the AWS Command Line Interface tool:

1. Download and install the AWS Command Line Interface tool.

2. Create a bucket for your data using the `!aws s3 mb s3://MyCloudData` command.

3. Upload your data using the `!aws s3 cp` command, specifying the path to your local data and the path to your Amazon S3 bucket.

Alternatively, you can use datastores to write data from MATLAB or cluster workers to Amazon S3. This method allows you to read data from Amazon S3, preprocess it, and then write it back to Amazon S3.

To use this method, create a datastore object that points to the URL of the Amazon S3 bucket, read the data into a tall array, preprocess it, and then write it back to Amazon S3 using the `write` function.