|
I’m excited to announce Amazon S3 Recordsdata, a brand new file system that seamlessly connects any AWS compute useful resource with Amazon Easy Storage Service (Amazon S3).
Greater than a decade in the past, as an AWS coach, I spent numerous hours explaining the elemental variations between object storage and file techniques. My favourite analogy was evaluating S3 objects to books in a library (you’ll be able to’t edit a web page, it’s essential to change the entire e-book) versus information in your laptop that you would be able to modify web page by web page. I drew diagrams, created metaphors, and helped clients perceive why they wanted totally different storage varieties for various workloads. Nicely, in the present day that distinction turns into a bit extra versatile.
With S3 Recordsdata, Amazon S3 is the primary and solely cloud object retailer that provides fully-featured, high-performance file system entry to your knowledge. It makes your buckets accessible as file techniques. This implies modifications to knowledge on the file system are routinely mirrored within the S3 bucket and you’ve got fine-grained management over synchronization. S3 Recordsdata will be hooked up to a number of compute sources enabling knowledge sharing throughout clusters with out duplication.
Till now, you had to decide on between Amazon S3 value, sturdiness, and the companies that may natively devour knowledge from it or a file system’s interactive capabilities. S3 Recordsdata eliminates that tradeoff. S3 turns into the central hub for all of your group’s knowledge. It’s accessible immediately from any AWS compute occasion, container, or operate, whether or not you’re operating manufacturing purposes, coaching ML fashions, or constructing agentic AI techniques.
You possibly can entry any basic goal bucket as a local file system in your Amazon Elastic Compute Cloud (Amazon EC2) cases, containers operating on Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS), or AWS Lambda features. The file system presents S3 objects as information and directories, supporting all Community File System (NFS) v4.1+ operations like creating, studying, updating, and deleting information.
As you’re employed with particular information and directories by way of the file system, related file metadata and contents are positioned onto the file system’s high-performance storage. By default, information that profit from low-latency entry are saved and served from the excessive efficiency storage. For information not saved on excessive efficiency storage resembling these needing giant sequential reads, S3 Recordsdata routinely serves these information immediately from Amazon S3 to maximise throughput. For byte-range reads, solely the requested bytes are transferred, minimizing knowledge motion and prices.
The system additionally helps clever pre-fetching to anticipate your knowledge entry wants. You’ve gotten fine-grained management over what will get saved on the file system’s excessive efficiency storage. You possibly can resolve whether or not to load full file knowledge or metadata solely, which implies you’ll be able to optimize on your particular entry patterns.
Below the hood, S3 Recordsdata makes use of Amazon Elastic File System (Amazon EFS) and delivers ~1ms latencies for energetic knowledge. The file system helps concurrent entry from a number of compute sources with NFS close-to-open consistency, making it very best for interactive, shared workloads that mutate knowledge, from agentic AI brokers collaborating by way of file-based instruments to ML coaching pipelines processing datasets.
Let me present you methods to get began.
Creating my first Amazon S3 file system, mounting, and utilizing it from an EC2 occasion is easy.
I’ve an EC2 occasion and a basic goal bucket. On this demo, I configure an S3 file system and entry the bucket from an EC2 occasion, utilizing common file system instructions.
For this demo, I exploit the AWS Administration Console. You can too use the AWS Command Line Interface (AWS CLI) or infrastructure as code (IaC).
Right here is the structure diagram for this demo.
Step 1: Create an S3 file system.
On the Amazon S3 part of the console, I select File techniques after which Create file system.
I enter the title of the bucket I need to expose as a file system and select Create file system.
Step 2: Uncover the mount goal.
A mount goal is a community endpoint that may reside in my digital non-public cloud (VPC). It permits my EC2 occasion to entry the S3 file system.
The console creates the mount targets routinely. I take notes of the Mount goal IDs on the Mount targets tab.
When utilizing the CLI, two separate instructions are essential to create the file system and its mount targets. First, I create the S3 file system with create-file-system. Then, I create the mount goal with create-mount-target.
Step 3: Mount the file system on my EC2 occasion.
After it’s linked to an EC2 occasion, I kind:
sudo mkdir /residence/ec2-user/s3files sudo mount -t s3files fs-0aa860d05df9afdfe:/ /residence/ec2-user/s3files
I can now work with my S3 knowledge immediately by way of the mounted file system in ~/s3files, utilizing normal file operations.
After I make updates to my information within the file system, S3 routinely manages and exports all updates as a brand new object or a brand new model on an present object again in my S3 bucket inside minutes.
Modifications made to things on the S3 bucket are seen within the file system inside just a few seconds however can typically take a minute or longer.
# Create a file on the EC2 file system
echo "Good day S3 Recordsdata" > s3files/good day.txt
# and confirm it is right here
ls -al s3files/good day.txt
-rw-r--r--. 1 ec2-user ec2-user 15 Oct 22 13:03 s3files/good day.txt
# See? the file can be on S3
aws s3 ls s3://s3files-aws-news-blog/good day.txt
2025-10-22 13:04:04 15 good day.txt
# And the content material is similar!
aws s3 cp s3://s3files-aws-news-blog/good day.txt . && cat good day.txt
Good day S3 RecordsdataIssues to know
Let me share some necessary technical particulars that I feel you’ll discover helpful.
One other query I steadily hear in buyer conversations is about choosing the proper file service on your workloads. Sure, I do know what you’re pondering: AWS and its seemingly overlapping companies, retaining cloud architects entertained throughout their structure evaluation conferences. Let me assist demystify this one.
S3 Recordsdata works greatest if you want interactive, shared entry to knowledge that lives in Amazon S3 by way of a excessive efficiency file system interface. It’s very best for workloads the place a number of compute sources—whether or not manufacturing purposes, agentic AI brokers utilizing Python libraries and CLI instruments, or machine studying (ML) coaching pipelines—have to learn, write, and mutate knowledge collaboratively. You get shared entry throughout compute clusters with out knowledge duplication, sub-millisecond latency, and computerized synchronization together with your S3 bucket.
For workloads migrating from on-premises NAS environments, Amazon FSx supplies the acquainted options and compatibility you want. Amazon FSx can be very best for high-performance computing (HPC) and GPU cluster storage with Amazon FSx for Lustre. It’s notably worthwhile when your purposes require particular file system capabilities from Amazon FSx for NetApp ONTAP, Amazon FSx for OpenZFS, or Amazon FSx for Home windows File Server.
Pricing and availability
S3 Recordsdata is out there in the present day in all business AWS Areas.
You pay for the portion of information saved in your S3 file system, for small file learn and all write operations to the file system, and for S3 requests throughout knowledge synchronization between the file system and the S3 bucket. The Amazon S3 pricing web page has all the small print.
From discussions with clients, I imagine S3 Recordsdata helps simplify cloud architectures by eliminating knowledge silos, synchronization complexity, and guide knowledge motion between objects and information. Whether or not you’re operating manufacturing instruments that already work with file techniques, constructing agentic AI techniques that depend on file-based Python libraries and shell scripts, or getting ready datasets for ML coaching, S3 Recordsdata lets these interactive, shared, hierarchical workloads entry S3 knowledge immediately with out selecting between the sturdiness of Amazon S3 and value advantages and a file system’s interactive capabilities. Now you can use Amazon S3 because the place for all of your organizations’ knowledge, figuring out the info is accessible immediately from any AWS compute occasion, container, and performance.
To study extra and get began, go to the S3 Recordsdata documentation.
I’d love to listen to how you employ this new functionality. Be happy to share your suggestions within the feedback beneath.
