Tag Archives: announcements

Enhanced Amazon S3 Integration for Amazon FSx for Lustre

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/enhanced-amazon-s3-integration-for-amazon-fsx-for-lustre/

Today, we are announcing two additional capabilities of Amazon FSx for Lustre. First, a full bi-directional synchronization of your file systems with Amazon Simple Storage Service (Amazon S3), including deleted files and objects. Second, the ability to synchronize your file systems with multiple S3 buckets or prefixes.

Lustre is a large scale, distributed parallel file system powering the workloads of most of the largest supercomputers. It is popular among AWS customers for high-performance computing workloads, such as meteorology, life-science, and engineering simulations. It is also used in media and entertainment, as well as the financial services industry.

I had my first hands-on Lustre file systems when I was working for Sun Microsystems. I was a pre-sales engineer and worked on some deals to sell multimillion-dollar compute and storage infrastructure to financial services companies. Back then, having access to a Lustre file system was a luxury. It required expensive compute, storage, and network hardware. We had to wait weeks for delivery. Furthermore, it required days to install and configure a cluster.

Fast forward to 2021, I may create a petabyte-scale Lustre cluster and attach the file system to compute resources running in the AWS cloud, on-demand, and only pay for what I use. There is no need to know about Storage Area Networks (SAN), Fiber Channel (FC) fabric, and other underlying technologies.

Modern applications use different storage options for different workloads. It is common to use S3 object storage for data transformation, preparation, or import/export tasks. Other workloads may require POSIX file-systems to access the data. FSx for Lustre lets you synchronize objects stored on S3 with the Lustre file system to meet these requirements.

When you link your S3 bucket to your file system, FSx for Lustre transparently presents S3 objects as files and lets you to write results back to S3.

Full Bi-Directional Synchronization with Multiple S3 Buckets
If your workloads require a fast, POSIX-compliant file system access to your S3 buckets, then you can use FSx for Lustre to link your S3 buckets to a file system and keep data synchronized between the file system and S3 in both directions. However, until today, there were a couple limitations. First, you had to manually configure a task to export data back from FSx for Lustre to S3. Second, deleted files on S3 were not automatically deleted from the file system. And third, an FSx for Lustre file system was synchronized with one S3 bucket only. We are addressing these three challenges with this launch.

Starting today, when you configure an automatic export policy for your data repository association, files on your FSx for Lustre file system are automatically exported to your data repository on S3. Next, deleted objects on S3 are now deleted from the FSx for Lustre file system. The opposite is also available: deleting files on FSx for Lustre triggers the deletion of corresponding objects on S3. Finally, you may now synchronize your FSx for Lustre file system with multiple S3 buckets. Each bucket has a different path at the root of your Lustre file system. For example your S3 bucket logs may be mapped to /fsx/logs and your other financial_data bucket may be mapped to /fsx/finance.

These new capabilities are useful when you must concurrently process data in S3 buckets using both a file-based and an object-based workflow, as well as share results in near real time between these workflows. For example, an application that accesses file data can do so by using an FSx for Lustre file system linked to your S3 bucket, while another application running on Amazon EMR may process the same files from S3.

Moreover, you may link multiple S3 buckets or prefixes to a single FSx for Lustre file system, thereby enabling a unified view across multiple datasets. Now you can create a single FSx for Lustre file system and easily link multiple S3 data repositories (S3 buckets or prefixes). This is convenient when you use multiple S3 buckets or prefixes to organize and manage access to your data lake, access files from a public S3 bucket (such as these hundreds of public datasets) and write job outputs to a different S3 bucket, or when you want to use a larger FSx for Lustre file system linked to multiple S3 datasets to achieve greater scale-out performance.

How It Works
Let’s create an FSx for Lustre file system and attach it to an Amazon Elastic Compute Cloud (Amazon EC2) instance. I make sure that the file system and instance are in the same VPC subnet to minimize data transfer costs. The file system security group must authorize access from the instance.

I open the AWS Management Console, navigate to FSx, and select Create file system. Then, I select Amazon FSx for Lustre. I am not going through all of the options to create a file system here, you can refer to the documentation to learn how to create a file system. I make sure that Import data from and export data to S3 is selected.

Lustre - enable S3 synchronizationIt takes a few minutes to create the file system. Once the status is ✅ Available, I navigate to the Data repository tab, and then select Create data repository association.

I choose a Data Repository path (my source S3 bucket) and a file system path (where in the file system that bucket will be imported).

FsX Lustre Data repository

Then, I choose the Import policy and Export policy. I may synchronize the creation of file/objects, their updates, and when they are deleted. I select Create.

FsX Lustre Data repository import policies

When I use automatic import, I also make sure to provide an S3 bucket in the same AWS Region as the FSx for Lustre cluster. FSx for Lustre supports linking to an S3 bucket in a different AWS Region for automatic export and all other capabilities.

Using the console, I see the list of Data repository associations. I wait for the import task status to become ✅ Succeeded. If I link the file system to an S3 bucket with a large number of objects, then I may choose to skip Importing metadata from repository while creating the data repository association, and then load metadata from selected prefixes in my S3 buckets that are required for my workload using an Import task.

FsX for lustre - meta data repository tasks

I create an EC2 instance in the same VPC subnet. Furthermore, I make sure that the FSx for Lustre cluster security group authorizes ingress traffic from the EC2 instance. I use SSH to connect to the instance, and then type the following commands (commands are prefixed with the $ sign that is part of my shell prompt).

# check kernel version, minimum version 4.14.104-95.84 is required 
$ uname -r
4.14.252-195.483.amzn2.aarch64

# install lustre client 
$ sudo amazon-linux-extras install -y lustre2.10
Installing lustre-client
...
Installed:
  lustre-client.aarch64 0:2.10.8-5.amzn2                                                                                                                        

Complete!

# create a mount point 
$ sudo mkdir /fsx

# mount the file system 
$ sudo mount -t lustre -o noatime,flock [email protected]:/ny345bmv /fsx

# verify mount succeeded
$ mount 
...
[email protected]:/ny345bmv on /fsx type lustre (rw,noatime,flock,lazystatfs)

Then, I verify that the file system contains the S3 objects, and I create a new file using the touch command.

Fsx Lustre - check file system

I switch to the AWS Console, under S3 and then my bucket name, and I verify that the file has been synchronized.

Fsx Lustre - check s3

Using the console, I delete the file from S3. And, unsurprisingly, after a few seconds, the file is also deleted from the FSx file system.

Fsx Lustre - check file systems - deleted

Pricing and Availability
These new capabilities are available at no additional cost on Amazon FSx for Lustre file systems. Automatic export and multiple repositories are only available on Persistent 2 file systems in US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland). Automatic import with support for deleted and moved objects in S3 is available on file systems created after July 23, 2020 in all regions where FSx for Lustre is available.

You can configure your file system to automatically import S3 updates by using the AWS Management Console, the AWS Command Line Interface (CLI), and AWS SDKs.

Learn more about using S3 data repositories with Amazon FSx for Lustre file systems.

One More Thing
One more thing while you are reading. Today, we also launched the next generation of FSx for Lustre file systems. FSx for Lustre next-gen file systems are built on AWS Graviton processors. They are designed to provide you with up to 5x higher throughput per terabyte (up to 1 GB/s per terabyte) and reduce your cost of throughput by up to 60% as compared to previous generation file systems. Give it a try today!

— seb

PS : my colleague Michael recorded a demo video to show you the enhanced S3 integration for FSx for Lustre in action. Check it out today.

Preview – AWS Backup Adds Support for Amazon S3

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/preview-aws-backup-adds-support-for-amazon-s3/

Starting today, you can preview AWS Backup for Amazon Simple Storage Service (Amazon S3).

AWS Backup is a fully managed, policy-based service that lets you to centralize and automate the backup and restore of your applications spanning across 12 AWS services: Amazon Elastic Compute Cloud (Amazon EC2) instances, Amazon Elastic Block Store (EBS) volumes, Amazon Relational Database Service (RDS) databases (including Amazon Aurora clusters), Amazon DynamoDB tables, Amazon Neptune databases, Amazon DocumentDB (with MongoDB compatibility) databases, Amazon Elastic File System (Amazon EFS) file systems, Amazon FSx for Lustre file systems, Amazon FSx for Windows File Server file systems, AWS Storage Gateway volumes, and now Amazon S3 (in preview).

Modern workloads and systems are leveraging different storage options for different functionalities. In the 21st century, it is normal to build applications relying on non-relational and relational databases, shared file storage, and object storage, just to name of few. When operating and managing these applications, you told us that you wanted centralized protection and provable compliance for application data stored in S3 alongside other AWS services for storage, compute, and databases.

I can see three benefits when integrating Amazon Simple Storage Service (Amazon S3) with your data protection policies in AWS Backup.

First, it lets you centrally manage your applications backups: AWS Backup provides an automated solution to centrally configure backup policies, thereby helping you simplify backup lifecycle management. This also makes it easy to ensure that your application data across AWS services (including S3) is centrally backed up.

Second, it lets you easily restore your data: AWS Backup provides a single-click-restore experience for your S3 data. This lets you perform point-in-time restores of your S3 buckets and objects to a new or existing S3 bucket.

Finally, it improves backup compliance: AWS Backup provides built-in dashboards that let you to track backup and restore operations for S3.

AWS Backup for S3 (Preview) lets you create continuous point-in-time backups along with periodic backups of S3 buckets, including object data, object tags, access control lists (ACLs), and user-defined metadata. The first backup is a full snapshot, while subsequent backups are incremental. If there is a data disruption event, then you choose a backup from the backup vault, and restore an S3 bucket (or individual S3 objects) to a new or existing S3 bucket. AWS Backup is integrated with AWS Organizations, which let you use a single policy across AWS accounts (within your Organizations) to automate backup creation and backup access management.

Furthermore, you can turn on AWS Backup Vault Lock to enable delete protection of the data that you protect with AWS Backup, and thereby improving protection of your immutable backups from accidental deletion or malicious re-encryption.

How to Get Started
AWS Backup works with versioned S3 buckets. Before you get started, turn on S3 Versioning on your buckets to backup.

I must enable S3 in AWS Backup Settings when I use this feature for the first time. Using the AWS Management Console, I navigate to AWS Backup, then select Settings and Configure resources. I enable S3, and select Confirm. This is a one-time operation.

AWS Backup - optin S3

For this demo, I already have an existing backup plan, and I want to add an S3 bucket to this plan. If you want to create a new backup plan, then you can refer to AWS Backup‘s technical documentation.

To start including my S3 objects in my backup plan, I open the AWS Management Console, navigate to Backup plans, and select Assign resources.

AWS Backup Add Resources

I give a name to my Resource assignment. I select Include specific resources types, then I select S3 as Resource type and one or several S3 Bucket names. When I am done, I select Assign resources.

Alternatively, I may use tags or resource IDs to assign S3 resources.

If you have thousands of S3 buckets, I recommend using tags to assign the S3 buckets to a backup plan. AWS Backup matches the tags in S3 buckets to the ones assigned to the backup plan, and it centrally backs up the S3 resources along with other AWS services that your application uses.

The other options are not different from what you know already.

AWS Backup - backup plan for S3

The Bucket names list in the previous screenshot only shows the S3 buckets in the same Region.

Alternatively, I may also create on-demand backups. I navigate to the Protected resources section, and select Create on-demand backup.

I select S3 as the Resource type, and select the Bucket name. As per usual, I choose a Backup Window, a Retention period, a Backup vault, and an IAM role. Then, I select Create on-demand backup.

AWS Backup - on-demand backup for S3After a while, depending on the size of my bucket, the backup is ✅ Completed.

AWS Backup for S3 - Backup completed

All of the backups are encrypted and stored securely in a backup vault that I selected in the backup plan.

A backup vault (or backup storage vault) is an encrypted logical construct in my AWS account that stores and organizes my backups (recovery points). I may create new backup vaults in every AWS Region where AWS Backup is available. I may enable AWS Backup Vault Lock (delete-protection capability) on the backup vault to avoid accidental deletions and prevent malicious actors from re-encrypting my data. AWS Backup stores my continuous backups and periodic snapshots in the backup vault of my preference, and it lets me browse and restore as per my requirements.

How to Restore Objects
Let’s try to restore this backup.

The restore operation is very flexible. I may restore entire S3 buckets or individual S3 objects. I may restore the backups to the source S3 bucket, or to another existing bucket. Furthermore, I may create a new S3 bucket during restore. The S3 buckets must have Versioning enabled. Also, I may change the encryption key during restore.

I navigate to Backup vaults to restore the S3 bucket I just backed up. In the Backups section, I select the Recovery point ID that I want to restore, and I select Restore from the Actions menu.

AWS Backup for S3 - restore

Before starting the restore, I may select a few options:

  • The Restore time: I may restore my continuous backup to a point-in-time in the last 35 days, while I can restore my periodic backups to their original state.
  • The Restore type: I may choose to restore the entire bucket or a subset of objects within it.
  • The Restore destination: I may choose to restore on the same bucket, on another one, or create a new bucket during restore.
  • The Restored object encryption: this lets me select the key I want to use to encrypt the restored objects in the bucket.

I select Restore backup to start the restore.

AWS Backup for S3 - restore optionsI can monitor the progress in the Jobs section, under the Restore jobs tab.

AWS Backup S3 - restore Jobs

When the status turns green to ✅ Completed, my objects are ready to use!

Generally, the most comprehensive data-protection strategies include regular testing and validation of your restore procedures before you need them. Testing your restores also helps to prepare and maintain recovery runbooks. In turn, that ensures operational readiness during a disaster recovery exercise, or an actual data loss scenario.

Availability and Pricing
The preview is available in the US West (Oregon) Region only.

During the preview, there are no charges for creating and storing backups. You will pay the AWS charges for underlying resources, such as S3 storage, API usage, and versioning.

Send us an email at [email protected] including your AWS account ID to register for the preview.

Go ahead and apply to the preview program today.

— seb

Amazon S3 Glacier is the Best Place to Archive Your Data – Introducing the S3 Glacier Instant Retrieval Storage Class

Post Syndicated from Marcia Villalba original https://aws.amazon.com/blogs/aws/amazon-s3-glacier-is-the-best-place-to-archive-your-data-introducing-the-s3-glacier-instant-retrieval-storage-class/

Today we are announcing the Amazon S3 Glacier Instant Retrieval storage class. This new archive storage class delivers the lowest cost storage for long-lived data that is rarely accessed and requires millisecond retrieval.

We are also excited to announce that S3 Intelligent-Tiering now automatically optimizes storage costs for rarely accessed data that needs immediate retrieval with the new Archive Instant Access tier, which is ideal for data with unknown or changing access patterns. For existing customers, this will provide an immediate savings of 68 percent for data that hasn’t been accessed for more than 90 days, with no action needed. The Frequent, Infrequent, and now Archive Instant Access tiers are designed for the same milliseconds access time and high-throughput performance.

In addition, we are announcing the new name for the existing Amazon S3 Glacier storage class and several price reductions.

Amazon S3 Glacier Instant Retrieval
The Amazon S3 Glacier storage classes are extremely low-cost and built for data archiving. They are secure and durable, and they are designed to provide the lowest cost for data that does not require immediate access, with retrieval options from minutes to hours.

Many customers need to store rarely accessed data for several years. However the data must be highly available and immediately accessible. Today, these customers use the S3 Standard-Infrequent Access (S3 Standard-IA) storage class. This storage class offers low cost for storage and allows customers to retrieve their data instantly.

S3 Glacier Instant Retrieval is a new storage class that delivers the fastest access to archive storage, with the same low latency and high-throughput performance as the S3 Standard and S3 Standard-IA storage classes. You can save up to 68 percent on storage costs as compared with using the S3 Standard-IA storage class when you use the S3 Glacier Instant Retrieval storage class and pay a low price to retrieve data. For example, in the US East (N. Virginia) Region, S3 Glacier Instant Retrieval storage pricing is $0.004 per GB-month and data retrieval is $0.03 per GB. Learn more about pricing for your Region.

Media archives, medical images, or user-generated content are just a few examples of ideal use cases for S3 Glacier Instant Retrieval. Once created, this content is rarely accessed, but when it is needed it must be available in milliseconds.

To get started using the new storage class from the Amazon S3 console, upload an object as you would normally, and select the S3 Glacier Instant Retrieval storage class.

Upload object with the new storage class

This feature is available programmatically from AWS SDKs, AWS Command Line Interface (CLI), and AWS CloudFormation.

In my opinion, the easiest way to store data in S3 Glacier Instant Retrieval is to use the S3 PUT API using the CLI. When using this API, set the storage class to GLACIER_IR.

aws s3api put-object --bucket <bucket-name> --key <object-key> --body <name-file> --storage-class GLACIER_IR

When the object is uploaded to Amazon S3, verify the storage class in the list of objects or on the object details page.

Storage classes

For data that already exists in Amazon S3, you can use S3 Lifecycle to transition data from the S3 Standard and S3 Standard-IA storage classes into S3 Glacier Instant Retrieval.

New Archive Instant Access Tier in S3 Intelligent-Tiering
S3 Intelligent-Tiering is a storage class that automatically moves objects between access tiers to optimize costs. This is the recommended storage class for data with unpredictable or changing access patterns, such as in data lakes, analytics, or user-generated content.

Until today, there were two low latency access tiers optimized for frequent and infrequent access, and two optional archive access tiers designed for asynchronous access optimized for rare access at a low cost.

Beginning today, the Archive Instant Access tier is added as a new access tier in the S3 Intelligent-Tiering storage class. You will start seeing automatic costs savings for your storage in S3 Intelligent-Tiering for rarely accessed objects.

The Archive Instant Access tier joins the group of low latency access tiers. This new tier is optimized for data that is not accessed for months at a time but, when it is needed, is available within milliseconds.

S3 Intelligent-Tiering automatically stores objects in three access tiers that deliver the same performance as the S3 Standard storage class:

  • Frequent Access tier
  • Infrequent Access tier
  • Archive Instant Access (new)

For a small monitoring and automation charge, S3 Intelligent-Tiering monitors access patterns and moves objects between the different access tiers. Objects that have not been accessed for 30 consecutive days are moved from the Frequent Access tier to the Infrequent Access tier for savings of 40 percent. When an object hasn’t been accessed for 90 consecutive days, S3 Intelligent-Tiering will move the object from the Infrequent Access tier to the Archive Instant Access tier, with a savings of 68 percent. If the data is accessed later, it is automatically moved back to the Frequent Access tier. No tiering charges apply when objects are moved between access tiers within the S3 Intelligent-Tiering storage class.

S3 Intelligent-Tiering access tiers

To get started with this new access tier, select Intelligent-Tiering as the storage class for an object when uploading an object using the S3 console. After 90 days of inactivity (30 days in Frequent Access tier and 60 days in Infrequent Access tier), S3 Intelligent-Tiering will automatically move the object to the Archive Instant Access tier. The introduction of the new Archive Instant Access tier has no impact on performance when you retrieve objects.

New name for the Amazon S3 Glacier storage class – S3 Glacier Flexible Retrieval
The existing Amazon S3 Glacier storage class is now named S3 Glacier Flexible Retrieval. This storage class now has free bulk retrievals in 5 to 12 hours, and the storage price has been reduced by 10 percent in all Regions, effective December 1, 2021. S3 Glacier Flexible Retrieval is now even more cost-effective, and the free bulk retrievals make it ideal for retrieving large data volumes.

These are the Amazon S3 archive storage classes:

  • S3 Glacier Instant Retrieval: The newest storage class is optimized for long-lived data that is rarely accessed (typically once per quarter). However when data is needed, it is available within milliseconds. For example, medical images and news media assets are perfect for this storage class.
  • S3 Glacier Flexible Retrieval: This newly renamed storage class is optimized for archiving data that can be retrieved in minutes or with free bulk retrievals in 5 to 12 hours. This storage class is ideal for backups and disaster recovery use cases, where you have large amounts of long-term, rarely accessed data, and you don’t want to worry about retrieval costs when you need the data.
  • S3 Glacier Deep Archive: This storage class is the lowest-cost storage in the cloud and is optimized for archiving data that can be restored in at least 12 hours. It’s great for storing your compliance archives or for digital media preservation.

Amazon S3 has reduced storage prices!
We are excited to announce that Amazon S3 has reduced storage prices of up to 31 percent in the S3 Standard-IA and S3 One Zone-IA storage classes across 9 AWS Regions: US West (N. California), Asia Pacific (Hong Kong), Asia Pacific (Mumbai), Asia Pacific (Osaka), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and South America (São Paulo). These price reductions are effective December 1, 2021.

Learn more about price reduction details.

Available Now
The new storage class, S3 Glacier Instant Retrieval, and the new Archive Instant Access tier in S3 Intelligent-Tiering are available today (November 30, 2021) in all AWS Regions.

The price cut for S3 Glacier and free bulk retrievals in all AWS Regions, and the S3 Standard-Infrequent Access/One Zone-Infrequent storage class in nine Regions will be effective on December 1, 2021.

Learn more about the storage classes changes and all the storage classes.

Marcia

New – Simplify Access Management for Data Stored in Amazon S3

Post Syndicated from Marcia Villalba original https://aws.amazon.com/blogs/aws/new-simplify-access-management-for-data-stored-in-amazon-s3/

Today, we are introducing a couple new features that simplify access management for data stored in Amazon Simple Storage Service (Amazon S3). First, we are introducing a new Amazon S3 Object Ownership setting that lets you disable access control lists (ACLs) to simplify access management for data stored in Amazon S3. Second, the Amazon S3 console policy editor now reports security warnings, errors, and suggestions powered by IAM Access Analyzer as you author your S3 policies.

Since launching 15 years ago, Amazon S3 buckets have been private by default. At first, the only way to grant access to objects was using ACLs. In 2011, AWS Identity and Access Management (IAM) was announced, which allowed the use of policies to define permissions and control access to buckets and objects in Amazon S3. Nowadays, you have several ways to control access to your data in Amazon S3, including IAM policies, S3 bucket policies, S3 Access Points policies, S3 Block Public Access, and ACLs.

ACLs are an access control mechanism in which each bucket and object has an ACL attached to it. ACLs define which AWS accounts or groups are granted access as well as the type of access. When an object is created, the ownership of it belongs to the creator.  This ownership information is embedded in the object ACL. When you upload an object to a bucket owned by another AWS account, and you want the bucket owner to access the object, then permissions need to be granted in the ACL. In many cases, ACLs and other kinds of policies are used within the same bucket.

The new Amazon S3 Object Ownership setting, Bucket owner enforced, lets you disable all of the ACLs associated with a bucket and the objects in it. When you apply this bucket-level setting, all of the objects in the bucket become owned by the AWS account that created the bucket, and ACLs are no longer used to grant access. Once applied, ownership changes automatically, and applications that write data to the bucket no longer need to specify any ACL. As a result, access to your data is based on policies. This simplifies access management for data stored in Amazon S3.

With this launch, when creating a new bucket in the Amazon S3 console, you can choose whether ACLs are enabled or disabled. In the Amazon S3 console, when you create a bucket, the default selection is that ACLs are disabled. If you wish to keep ACLs enabled, you can choose other configurations for Object Ownership, specifically:

  • Bucket owner preferred: All new objects written to this bucket with the bucket-owner-full-controlled canned ACL will be owned by the bucket owner. ACLs are still used for access control.
  • Object writer: The object writer remains the object owner. ACLs are still used for access control.

Options for object ownership

For existing buckets, you can view and manage this setting in the Permissions tab.

Before enabling the Bucket owner enforced setting for Object Ownership on an existing bucket, you must migrate access granted to other AWS accounts from the bucket ACL to the bucket policy. Otherwise, you will receive an error when enabling the setting. This helps you ensure applications writing data to your bucket are uninterrupted. Make sure to test your applications after you migrate the access.

Policy validation in the Amazon S3 console
We are also introducing policy validation in the Amazon S3 console to help you out when writing resource-based policies for Amazon S3. This simplifies authoring access control policies for Amazon S3 buckets and access points with over 100 actionable policy checks powered by IAM Access Analyzer.

To access policy validation in the Amazon S3 console, first go to the detail page for a bucket. Then, go to the Permissions tab and edit the bucket policy.

Accessing the IAM Policy Validation in S3 consoleWhen you start writing your policy, you see that, as you type, different findings appear at the bottom of the screen. Policy checks from IAM Access Analyzer are designed to validate your policies and report security warnings, errors, and suggestions as findings based on their impact to help you make your policy more secure.

You can also perform these checks and validations using the IAM Access Analyzer’s ValidatePolicy API.

Example of policy suggestion

Availability
Amazon S3 Object Ownership is available at no additional cost in all AWS Regions, excluding the AWS China Regions and AWS GovCloud Regions. IAM Access Analyzer policy validation in the Amazon S3 console is available at no additional cost in all AWS Regions, including the AWS China Regions and AWS GovCloud Regions.

Get started with Amazon S3 Object Ownership through the Amazon S3 console, AWS Command Line Interface (CLI), Amazon S3 REST API, AWS SDKs, or AWS CloudFormation. Learn more about this feature on the documentation page.

And to learn more and get started with policy validation in the Amazon S3 console, see the Access Analyzer policy validation documentation.

Marcia

New for AWS Backup – Support for VMware and VMware Cloud on AWS

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-aws-backup-support-for-vmware-and-vmware-cloud-on-aws/

Today, I am happy to announce AWS Backup support for VMware, a new capability that enables you to centralize and automate data protection of virtual machines (VMs) running on VMware on premises and VMware CloudTM on AWS. You can now use a single, centrally managed policy in AWS Backup to protect these VMware environments together with 12 AWS compute, storage, and database services already supported by AWS Backup. You can then use AWS Backup to restore VMware workloads to on-premises data centers and VMware Cloud on AWS.

While doing so, AWS Backup Audit Manager lets you consistently demonstrate compliance by monitoring backup, copy, and restore operations and generating auditor-ready reports to satisfy your data governance and regulatory requirements.

Let’s see how this works in practice.

Using AWS Backup Support for VMware
There are three steps to back up VMware virtual machines (VMs) with AWS Backup:

  1. Create a gateway to connect AWS Backup to your hypervisor.
  2. Connect to your hypervisor through the gateway.
  3. Assign virtual machines managed by your hypervisor to a backup plan.

AWS Back Support for VMware diagram

On the left pane of the AWS Backup console, there is a new External resources section. There, I choose Gateways and then Create gateway. This AWS Backup gateway helps with discovery of the on-premises VMware environment and acts as a cloud gateway to send and receive data.

I download the Open Virtualization Format (OVF) file of the AWS Backup gateway and follow the instructions to deploy the gateway using the VMware vSphere client. I am using an internal test and development VMware environment for this walkthrough.

VMware vCenter screenshot.

After deploying the gateway in my VMware environment, I come back to the AWS Backup console. I write a name for the gateway (for simplicity, I use the same name of the gateway VM) and the IP address of the gateway VM. Optionally, I can add tags to help organize and track my setup. I go on and create the gateway.

Console screenshot.

Now, I choose Add hypervisor. I write a name for the hypervisor and the IP address of the VMware vCenter server host.

Console screenshot.

I enter the username and password of a service account that I created for AWS Backup on the Active Directory domain. The username should include the domain (for example, [email protected]). Then, I choose the encryption key to protect the service account credentials. If I don’t choose my own AWS Key Management Service (KMS) key, AWS Backup encrypts the username and password using a key that AWS owns and manages.

Console screenshot.

I select the gateway to connect to the hypervisor and choose Test gateway connection. This test helps ensure that the gateway can communicate with the hypervisor before I complete the configuration. Optionally, I can add tags to help organize and track my setup. I go on and add the hypervisor.

Console screenshot.

After a few minutes, the hypervisor is online, and I see the VMs managed by vCenter in the AWS Backup console. I can now use these virtual machines as resources in my backup plans in the same way as the other AWS compute, storage, and database resources supported by AWS Backup.

Console screenshot.

I create a new backup plan and start with a template. The rules of the template enforce daily backups with five weeks of retention and monthly backups with one year of retention. I can customize these rules based on my requirements.

Console screenshot.

Then, I choose to assign resources to the backup plan, and I select three VMs.

Console screenshot.

If you need, you can create an on-demand backup in the Protected resources section of the console. For example, here I am starting the on-demand backup for one of the VMs.

Console screenshot.

When a backup is complete, VMs are added to the list of the protected resources, and I can initiate a restore.

Console screenshot.

I select the backup and choose Restore. Then, I enter the restore location, which can be the same VMware environment I used for the backup or another (for example, on VMware Cloud on AWS). Below, I specify name, path, compute resource name, and datastore to use for the restore. Then, I choose Restore backup.

Console screenshot.

I monitor the status of my backup and restore jobs from the AWS Backup console. To monitor backup and restore metrics over a period of time, I can use Amazon CloudWatch metrics, logs, and alarms. I can also send events to Amazon EventBridge to receive notifications once a job completes or fails.

Availability and Pricing
AWS Backup support for VMware is available in the US East (N. Virginia, Ohio), US West (N. California, Oregon), GovCloud (US-East, US-West), Canada (Central), Europe (Frankfurt, Ireland, London, Milan, Paris, Stockholm), South America (São Paulo), Asia Pacific (Hong Kong, Mumbai, Seoul, Singapore, Sydney, Tokyo, Osaka), Middle East (Bahrain), and Africa (Cape Town) Regions. Please see the AWS Regional Services List for more information.

AWS Backup supports VMware ESXi 6.7.x and 7.0.x VMs running on NFS, VMFS, and VSAN data stores on premises and in VMware Cloud on AWS. In addition, AWS Backup supports both SCSI Hot-Add and Network Block Device (NBD) transport modes for copying data from source VMs to AWS.

With AWS Backup support for VMware, you pay using the same dimensions that AWS Backup uses today: backup storage, restore, and cross-region data transfer. For more information, see the AWS Backup pricing page.

Your VM backups are stored in a backup vault. All backups stored and managed by AWS Backup are replicated to 3 Availability Zones (AZs) in the Region and designed for 99.999999999 percent (11 9s) durability and 99.99 percent (4 9s) of service availability.

AWS Backup supports first full, then incremental-forever, backups of VMs that you can create on-demand or via a schedule configured in your backup plan. AWS Backup always does full restores even though backups are stored as incremental, enabling you to benefit from storage efficiency cost savings while easily performing restores.

Centrally protect your VMware environments and your AWS compute, storage, and database resources with AWS Backup.

Danilo

New – Amazon FSx for OpenZFS

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-amazon-fsx-for-openzfs/

Last month, my colleague Bill Vass said that we are “slowly adding additional file systems” to Amazon FSx. I’d question Bill’s definition of slow, given that his team has launched Amazon FSx for Lustre, Amazon FSx for Windows File Server, and Amazon FSx for NetApp ONTAP in less than three years.

Amazon FSx for OpenZFS
Today I am happy to announce Amazon FSx for OpenZFS, the newest addition to the Amazon FSx family. Just like the other members of the family, this new addition lets you use a popular file system without having to deal with hardware provisioning, software configuration, patching, backups, and the like. You can create a file system in minutes and begin to enjoy the benefits of OpenZFS right away: transparent compression, continuous integrity verification, snapshots, and copy-on-write. Even better, you get all of these benefits without having to develop the specialized expertise that has traditionally been needed to set up and administer OpenZFS.

FSx for OpenZFS is powered by the AWS Graviton family processors and AWS SRD (Scalable Reliable Datagram) Networking, and can deliver up to 1 million IOPS with latencies of 100-200 microseconds, along with up to 4 GB/second of uncompressed throughput, up to 12 GB/second of compressed throughput, and up to 12.5 GB/second throughput to cached data. FSx for OpenZFS supports the OpenZFS Adaptive Replacement Cache (ARC) and uses memory in the file server to provide faster performance. It also supports advanced NFS performance features such as session trunking and NFS delegation, allowing you to get very high throughput and IOPS from a single client, while still safely caching frequently accessed data on the client side.

FSx for OpenZFS volumes can be accessed from cloud or on-premises Linux, MacOS, and Windows clients via industry-standard NFS protocols (v3, v4, v4.1, and v4.2). Cloud clients can be Amazon Elastic Compute Cloud (Amazon EC2) instances, Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Kubernetes Service (EKS) clusters, Amazon WorkSpaces virtual desktops, and VMware Cloud on AWS. Your data is stored in encrypted form and replicated within an AWS Availability Zone, with components replaced automatically and transparently as necessary.

You can use FSx for OpenZFS to address your highly demanding machine learning, EDA (Electronic Design Automation), media processing, financial analytics, code repository, DevOps, and web content management workloads. With performance that is close to local storage, FSx for OpenZFS is great for these and other latency-sensitive workloads that manipulate and sequentially access many small files. Finally, because you can create, mount, use, and delete file systems as needed, you can now use OpenZFS in a dynamic, agile fashion.

Using Amazon FSx for OpenZFS
I can create an OpenZFS file system using the AWS Management Console, CLI, APIs, or AWS CloudFormation. From the FSx Console I click Create file system and choose Amazon FSx for OpenZFS:

I can choose Quick create (and use recommended best-practice configurations), or Standard create (and set all of the configuration options myself). I’ll take the easy route and use the recommended best practices to get started. I enter a name (Jeff-OpenZFS) select the amount of SSD storage that I need, choose a VPC & subnet, and click Next:

The console shows me that I can edit many of the attributes of my file system later if necessary. I review the settings and click Create file system:

My file system is ready within a minute or two, and I click Attach to get the proper commands to mount it to my client:

To be more precise, I am mounting the root volume (/fsx) of my file system. Once it is mounted, I can use it as I would any other file system. After I add some files to it, I can use the Action menu in the console to create a backup:

I can restore the backup to a new file system:

As I noted earlier, each file system can deliver up to 4 gigabytes per second of throughput for uncompressed data. I can look at total throughput and other metrics in the console:

I can set throughput capacity of each volume when I create it, and then change it later if necessary:

Changes take effect within minutes. The file system remains active and mounted while the change is put into effect, but some operations may pause momentarily:

A single OpenZFS file system can contain multiple volumes, each with separate quotas (overall volume storage, per-user storage, and per-group storage) and compression settings. When I use the quick create option a root volume named fsx is created for me; I can click Create volume to create more volumes at any time:

The new volume exists within the namespace hierarchy of the parent, and can be mounted separately or accessed from the parent.

Things to Know
Here are a couple of quick facts and to wrap up this post:

Pricing – Pricing is based on the provisioned storage capacity, throughput, and IOPS.

Regions – Amazon FSx for OpenZFS is available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Canada (Central), Asia Pacific (Tokyo), and Europe (Frankfurt) Regions.

In the Works – We are working on additional features including storage scaling, IOPS scaling, a high availability option and another storage class.

Now Available
Amazon FSx for OpenZFS is available now and you can start using it today!

Jeff;

AWS Nitro SSD – High Performance Storage for your I/O-Intensive Applications

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-nitro-ssd-high-performance-storage-for-your-i-o-intensive-applications/

We love to solve difficult problems for our customers! As you have seen through the years, innovation at AWS takes many forms, and encompasses both hardware and software.

One of my favorite examples of customer-driven innovation is AWS Nitro System, which I first wrote about back in mid-2018. In that post I told you how Nitro System would allow us to innovate more quickly than ever, with the goal of creating instances that would run even more types of workloads. I also shared the basic building blocks, as they existed at that time, including Nitro Cards to accelerate and offload network and storage I/O, the Nitro Security Chip to monitor and protect hardware resources, and the Nitro Hypervisor to manage memory and CPU allocation with very low overhead.

Today I would like to tell you about one more building block!

AWS Nitro SSD
For decades, traditional hard drives (sometimes jokingly referred to as spinning rust) were the primary block storage devices. Today, while spinning rust still has its place, most high-performance storage is based on more modern Solid State Drives (SSD). Open up an SSD and you will find lots of flash memory and a firmware-driven processor that manages access to the memory and supports higher-level functions such as block mapping, encryption, caching, wear leveling, and so forth.

The scale of the AWS Cloud and the range of customer use cases that it supports gives us some valuable insights into the ways that today’s applications, database engines, and operating systems make use of block storage. As a result, after delivering several generations of EC2 instances we saw an opportunity to do better. Our goal was to allow I/O-intensive workloads (relational databases, NoSQL databases, data warehouses, search engines, and analytics engines to name a few) to run faster and with more predictable performance.

Today I would like to tell you about the AWS Nitro SSD. The first generation of these devices were used to power io2 Block Express EBS volumes, and allow us to give you EBS volumes with lots of IOPS, plenty of throughput, and a maximum volume size of 64 TiB. The Im4gn and Is4gen instances that I wrote about earlier today make use of the second generation of AWS Nitro SSDs, as will many future EC2 instances, including the I4i instances that we preannounced today.

The AWS Nitro SSDs are designed to be installed and to operate at cloud scale. While this sounds like a simple exercise in manufacturing and installing more devices, the reality is a lot more complex and a lot more interesting. As I noted earlier, the firmware inside of each device is responsible for implementing many lower-level functions. As our customers push the devices to their limits, they expect us to be able to diagnose and resolve any performance inconsistencies they observe. Building our own devices allows us to design in operational telemetry and diagnostics, along with mechanisms that enable us to install firmware updates at cloud scale & at cloud speed. Taking this even further, we developed our own code to manage the instance-level storage in order to further improve the reliability and debug-ability, and to deliver consistent performance.

On the performance side, our deep understanding of cloud workloads led us to engineer the devices so that they can deliver maximum performance under a sustained, continuous load. SSDs are built from fast, dense flash memory. Due to the characteristics of this semiconductor memory, each cell can only be written, erased, and then rewritten a limited number of times. In order to make the devices last as long as possible, the firmware is responsible for a process known as wear leveling. I don’t understand the details, but I assume that this includes some sort of mapping from logical block numbers to physical cells in a way that evens out the number of cycles over time. There’s some housekeeping (a form of garbage collection) involved in this process, and garden-variety SSDs can slow down (creating latency spikes) at unpredictable times when dealing with a barrage of writes. We also took advantage of our database expertise and built a very sophisticated, power-fail-safe journal-based database into the SSD firmware.

The second generation of AWS Nitro SSDs were designed to avoid latency spikes and deliver great I/O performance on real-world workloads. Our benchmarks show instances that use the AWS Nitro SSDs, such as the new Im4gn and Is4gen, deliver 75% lower latency variability than I3 instances, giving you more consistent performance.

Putting all of this together, there’s a very tight, rapidly rotating flywheel in action here because the team that builds the Nitro SSDs is part of the AWS storage team, and also has operational responsibilities. Like all teams at AWS, they watch the metrics day-in and day-out, and can efficiently deploy new firmware using a CI/CD model.

Join the Team
As is always the case, there’s always more innovation ahead, and we have some awesome positions on the teams that design the AWS Nitro SSDs. For example:

Jeff;

New Storage-Optimized Amazon EC2 Instances (Im4gn and Is4gen) Powered by AWS Graviton2 Processors

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-storage-optimized-amazon-ec2-instances-im4gn-and-is4gen-powered-by-aws-graviton2-processors/

EC2 storage-optimized instances are designed to deliver high disk I/O performance, and plenty of storage. Our customers use them to host high-performance real-time databases, distributed file systems, data warehouses, key-value stores, and more. Over the years we have released multiple generations of storage-optimized instances including the HS1 (2012) , D2 (2015), I2 (2013) , I3 (2017), I3en (2019), and D3/D3en (2020).

As I look back on all of these launches, it is interesting to see how we continue to provide an ever-increasing set of options that make each successive generation an even better fit for the diverse (and also ever-increasing) needs of our customers. HS1 instances were available in just one size, D2 and I2 in four, I3 in six, and I3en in eight. These instances give our customers the freedom to choose the size that best meets their current needs while also giving them room to scale up or down if those needs happen to change.

Im4gn and Is4gen
Today I am happy to introduce the two newest families of storage-optimized instances, Im4gn and Is4gen, powered by Graviton2 processors. Both instances offer up to 30 TB of NVMe storage using AWS Nitro SSD devices that are custom-built by AWS. As part of our drive to innovate on behalf of our customers, we turned our attention to storage and designed devices that were optimized to support high-speed access to large amounts of data. The AWS Nitro SSDs reduce I/O latency by up to 60% and also reduce latency variability by up to 75% when compared to the third generation of storage-optimized instances. As a result you get faster and more predictable performance for your I/O-intensive EC2 workloads.

Im4gn instances are a great fit for applications that require large amounts of dense SSD storage and high compute performance, but are not especially memory intensive such as social games, session storage, chatbots, and search engines. Here are the specs:

Instance Name vCPUs
Memory Local NVMe Storage
(AWS Nitro SSD)
Read Throughput
(128 KB Blocks)
EBS-Optimized Bandwidth Network Bandwidth
im4gn.large 2 8 GiB 937 GB 250 MB/s Up to 9.5 Gbps Up to 25 Gbps
im4gn.xlarge 4 16 GiB 1.875 TB 500 MB/s Up to 9.5 Gbps Up to 25 Gbps
im4gn.2xlarge 8 32 GiB 3.75 TB 1 GB/s Up to 9.5 Gbps Up to 25 Gbps
im4gn.4xlarge 16 64 GiB 7.5 TB 2 GB/s 9.5 Gbps 25 Gbps
im4gn.8xlarge 32 128 GiB 15 TB
(2 x 7.5 TB)
4 GB/s 19 Gbps 50 Gbps
im4gn.16xlarge 64 256 GiB 30 TB
(4 x 7.5 TB)
8 GB/s 38 Gbps 100 Gbps

Im4gn instances provide up to 40% better price performance and up to 44% lower cost per TB of storage compared to I3 instances. The new instances are available in the AWS US West (Oregon), US East (Ohio), US East (N. Virginia), and Europe (Ireland) Regions as On-Demand, Spot, Savings Plan, and Reserved instances.

Is4gen instances are a great fit for applications that do large amounts of random I/O to large amounts of SSD storage. This includes shared file systems, stream processing, social media monitoring, and streaming platforms, all of which can use the increased storage density to retain more data locally. Here are the specs:

Instance Name vCPUs
Memory Local NVMe Storage
(AWS Nitro SSD)
Read Throughput
(128 KB Blocks)
EBS-Optimized Bandwidth Network Bandwidth
is4gen.medium 1 6 GiB 937 GB 250 MB/s Up to 9.5 Gbps Up to 25 Gbps
is4gen.large 2 12 GiB 1.875 TB 500 MB/s Up to 9.5 Gbps Up to 25 Gbps
is4gen.xlarge 4 24 GiB 3.75 TB 1 GB/s Up to 9.5 Gbps Up to 25 Gbps
is4gen.2xlarge 8 48 GiB 7.5 TB 2 GB /s Up to 9.5 Gbps Up to 25 Gbps
is4gen.4xlarge 16 96 GiB 15 TB
(2 x 7.5 TB)
4 GB/s 9.5 Gbps 25 Gbps
is4gen.8xlarge 32 192 GiB 30 TB
(4 x 7.5 TB)
8 GB/s 19 Gbps 50 Gbps

Is4gen instances provide 15% lower cost per TB of storage and up to 48% better compute performance compared to I3en instances. The new instances are available in the AWS US West (Oregon), US East (Ohio), US East (N. Virginia), and Europe (Ireland) Regions as On-Demand, Spot, Savings Plan, and Reserved instances.

Available Now
As I never get tired of saying, these new instances are available now and you can start using them today. You can use Amazon Linux 2, Ubuntu 18.04.05 (and newer), Red Hat Enterprise Linux 8.0, and SUSE Enterprise Server 15 (and newer) AMIs, along with the container-optimized ECS and EKS AMIs. Learn more about the Im4gn and Is4gen instances.

Jeff;

PS – As of this launch twelve EC2 instance types are now powered by Graviton2 processors! To learn more, visit the Graviton2 page.

New for AWS Control Tower – Region Deny and Guardrails to Help You Meet Data Residency Requirements

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-for-aws-control-tower-region-deny-and-guardrails-to-help-you-meet-data-residency-requirements/

Many customers, such as those in highly regulated industries and the public sector, want to have control over where their data is stored and processed. AWS already offers many tools and features to comply with local laws and regulations, but we want to provide a simplified way to translate data residency requirements into controls that can be applied to single- and multi-account environments.

Starting today, you can use AWS Control Tower to deploy data residency preventive and detective controls, referred to as guardrails. These guardrails will prevent provisioning resources in unwanted AWS Regions by restricting access to AWS APIs through service control policies (SCPs) built and managed by AWS Control Tower. In this way, content cannot be created or transferred outside of your selected Regions at the infrastructure level. In this context, content can be software (including machine images), data, text, audio, video, or images hosted on AWS for processing or storage. For example, AWS customers in Germany can deny access to AWS services in Regions outside of Frankfurt with the exception of global services such as AWS Identity and Access Management (IAM) and AWS Organizations.

AWS Control Tower also offers guardrails to further control data residency in underlying AWS service options, for example, blocking Amazon Simple Storage Service (Amazon S3) cross-region replication or blocking the creation of internet gateways.

The AWS account used for managing AWS Control Tower is not restricted by the new Region deny settings. That account can be used for remediation if you have data in an unwanted Region before enabling Region deny.

Detective guardrails are implemented via AWS Config rules and can further detect unexpected configuration changes that should not be allowed.

You still retain a shared responsibility model for data residency at the application level, but these controls can help you restrict what infrastructure and application teams can do on AWS.

Using Data Residency Guardrails in AWS Control Tower
To use the new data residency guardrails, you need to have created a landing zone using AWS Control Tower. See Plan your AWS Control Tower landing zone for more information.

To see all the new controls that are available, I select Guardrails on the left pane of the AWS Control Tower console and then find those in the Data Residency category. I sort results by Behavior. Guardrails that have a Prevention behavior are implemented as SCPs. Those that have a Detection behavior are implemented as AWS Config rules.

Console screenshot.

The most interesting guardrail is probably the one denying access to AWS based on the requested AWS Region. I choose it from the list and find that it is different from the other guardrails because it affects all Organizational Units (OUs) and cannot be activated here but must be activated in the landing zone settings.

Console screenshot.

Below the Overview, in the Guardrail components, there is a link to the full SCP for this guardrail, and I can see the list of the AWS APIs that, when this setting is enabled, are still going to be allowed towards non-governed Regions. Depending on your requirements, some of those services, such as Amazon CloudFront or AWS Global Accelerator, can be further limited by a custom SCP.

In the Landing zone settings, the Region deny guardrail is currently not enabled. I choose Modify settings and then enable the Region deny settings.

Console screenshot.

Below the Region deny settings, there is the list of AWS Regions governed by the landing zone. Those will be the regions allowed when I enable Region deny.

Console screenshot.

In my case, I have four governed Regions, two in the US and two in Europe:

  • US East (N. Virginia), which is also the home Region for the landing zone
  • US West (Oregon)
  • Europe (Ireland)
  • Europe (Frankfurt)

I choose Update landing zone at the bottom. The update of the landing zone takes a few minutes to complete. Now, the vast majority of the AWS APIs are blocked if they are not directed to one of those governed Regions. Let’s do a few tests.

Testing Region Deny in a Sandbox Account
Using AWS Single Sign-On, I copy the AWS credentials to use the sandbox account with AWSAdministratorAccess permissions. In a terminal, I paste the commands setting the environment variables to use those credentials.

Console screenshot.

Now, I try to start a new Amazon Elastic Compute Cloud (Amazon EC2) instance in US East (Ohio), one of the non-governed Regions. In a landing zone, the default VPC is replaced by a VPC managed by AWS Control Tower. To start the instance, I need to specify a VPC subnet. Let’s find a subnet ID that I can use.

aws ec2 describe-subnets --query 'Subnets[0].SubnetId' --region us-east-2

An error occurred (UnauthorizedOperation) when calling the DescribeSubnets operation:
You are not authorized to perform this operation.

As expected, I am not authorized to perform this operation in US East (Ohio). Let’s try to start an EC2 instance without passing the subnet ID.

aws ec2 run-instances --image-id ami-0dd0ccab7e2801812 --region us-east-2 \
    --instance-type t3.small                                     

An error occurred (UnauthorizedOperation) when calling the RunInstances operation:
You are not authorized to perform this operation.
Encoded authorization failure message: <ENCODED MESSAGE>

Again, I am not authorized. More information is included in the encoded authorization failure message that I can decode as described in this article:

aws sts decode-authorization-message --encoded-message <ENCODED MESSAGE>

The decoded message (that I have omitted for brevity) tells me that there was an explicit deny to my request and includes the full SCP that caused the deny. This information is really useful for debugging these kind of errors.

Now, let’s try in US East (N. Virginia), one of the four governed regions.

aws ec2 describe-subnets --query 'Subnets[0].SubnetId' --region us-east-1
"subnet-0f3580c0c5e56c210"

This time, the command returns the subnet ID of the first subnet returned by the request. Let’s start an instance in US East (N. Virginia) using this subnet.

aws ec2 run-instances --image-id  ami-04ad2567c9e3d7893 --region us-east-1 \
    --instance-type t3.small --subnet-id subnet-0f3580c0c5e56c210

As expected, it works, and I can see the EC2 instance running in the console.

Console screenshot.

Similarly, APIs for other AWS services are limited by the Region deny settings. For example, I can’t create an S3 bucket in a non-governed Region.

Console screenshot.

When I try to create the bucket, I get an access denied error.

Console screenshot.

As expected, the creation of an S3 bucket works in a governed Region.

Even if someone gives this account access to a bucket in a non-governed Region, I would not be able to copy any data into that bucket.

Other preventive guardrails can enforce data residency, for example:

  • Disallow cross-region networking for Amazon EC2, Amazon CloudFront, and AWS Global Accelerator
  • Disallow internet access for an Amazon VPC instance managed by a customer
  • Disallow Amazon Virtual Private Network (VPN) connections

Now, let’s see how detective guardrails work.

Testing Detective Guardrails in a Sandbox Account
I enable the following guardrails for all accounts in the sandbox OU:

  • Detect whether Amazon EBS snapshots are restorable by all AWS accounts
  • Detect whether public routes exist in the route table for an internet gateway

Now, I want to see what happens if I go against these guardrails. In the EC2 console, I create an EBS snapshot for the volume of the EC2 instance I started before. Then, I modify permissions to share it with all AWS accounts.

Console screenshot.

Then, in the VPC console, I create an internet gateway, attach it to the AWS Control Tower managed VPC, and update the route table of one of the private subnets to use the internet gateway.

Console screenshot.

After a few minutes, the noncompliant resources in the sandbox account are found by the detective guardrails.

Console screenshot.

I look at the information provided by the guardrails and update my configuration to fix the issues. In a multi-account setup I’d contact the account owner and ask for remediation.

Availability and Pricing
You can use data-residency guardrails to control resources in any AWS Region. To create a landing zone, you should start from one of the Regions where AWS Control Tower is offered. For more information, see the AWS Regional Services List. There is no additional cost for this feature. You pay the costs of other services used, such as AWS Config.

This feature provides you with a framework of controls and guidance for setting up a multi-account environment that addresses data residency requirements. Depending on your use case, you may use any subset of the new data residency guardrails.

Set up guardrails based on your data residency requirements with AWS Control Tower.

Danilo

Announcing Amazon SageMaker Canvas – a Visual, No Code Machine Learning Capability for Business Analysts

Post Syndicated from Alex Casalboni original https://aws.amazon.com/blogs/aws/announcing-amazon-sagemaker-canvas-a-visual-no-code-machine-learning-capability-for-business-analysts/

As an organization facing business problems and dealing with data on a daily basis, the ability to build systems that can predict business outcomes becomes very important. This ability lets you solve problems and move faster by automating slow processes and embedding intelligence in your IT systems.

But how do you make sure that all teams and individual decision makers in the organization are empowered to create these machine learning (ML) systems at scale, and without depending on other data science and data engineering teams? As a business user or data analyst, you’d like to build and use prediction systems based on the data that you analyze and process every day, without having to learn about hundreds of algorithms, training parameters, evaluation metrics, and deployment best practices.

Today, I’m excited to announce the general availability of Amazon SageMaker Canvas, a new visual, no code capability that allows business analysts to build ML models and generate accurate predictions without writing code or requiring ML expertise. Its intuitive user interface lets you browse and access disparate data sources in the cloud or on-premises, combine datasets with the click of a button, train accurate models, and then generate new predictions once new data is available.

SageMaker Canvas leverages the same technology as Amazon SageMaker to automatically clean and combine your data, create hundreds of models under the hood, select the best performing one, and generate new individual or batch predictions. It supports multiple problem types such as binary classification, multi-class classification, numerical regression, and time series forecasting. These problem types let you address business-critical use cases, such as fraud detection, churn reduction, and inventory optimization, without writing a single line of code.

SageMaker Canvas in Action
Imagine that I’m an e-commerce manager who needs to predict whether or not a product will be shipped on time. The datasets at my disposal consist of a product catalog and the historical shipping dataset, both in CSV format.

First, I enter the SageMaker Canvas application where all of my models and datasets are created and inspected.

I select Import, and upload two CSV files: ProductData.csv and ShippingData.csv. I have 120 products and 10,000 shipping records.

I could also fetch data from Amazon Simple Storage Service (Amazon S3) or connect to other cloud or on-premises data sources, such as Amazon Redshift or Snowflake. For this use case, I prefer to upload 1.6 MB of data directly from my computer.

Before confirming the import, I have a chance to preview the two datasets, their columns, and their respective values. For example, each product has a ComputerBrand, ScreenSize, and PackageWeight. In addition to useful columns such as ShippingOrigin, OrderDate, and ShippingPriority, each record in the shipping dataset also contains OnTimeDelivery, which is either On Time or Late. This column will be used by SageMaker Canvas to generate a prediction model based on historical data.

After a few seconds of processing, the datasets are ready, and I decide to join them to create a single dataset containing both product and shipping information. This is an optional step that often lets you increase the precision of a prediction model.

Now I can simply drag and drop the two datasets: SageMaker Canvas will automatically identify the shared ProductId column and apply an Inner Join transformation.

The join preview lets me visualize the resulting columns, identify missing or invalid values, and optionally deselect unwanted columns.

I select Save joined data and provide a new name for this joined dataset, which now includes 16 columns and 10,000 records.

Next, I want to create a model and start by selecting New model in the Models section on the left menu. I call it On Time Prediction Model.

The first step is selecting a dataset.

I select a target column that my model will predict: OnTimeDelivery.

SageMaker Canvas shows me the value distribution and already recommends the most appropriate model type: two categories classification.

Before proceeding with the model training, I have the option to generate an analysis report. This analysis gives me two very important pieces of information: the estimated accuracy and the impact of each column.

The estimated accuracy of 99.9% gives me confidence, but then I notice that the highest impact is provided by the ActualShippingDays column. Unfortunately, this column is not available in advance and I can’t use it for my predictions. So I deselect it and run the analysis again.

The new estimated accuracy is 94.2%, which is still pretty high. The most impactful columns are ShippingPriority, YShippingDistance, XShippingDistance, and Carrier. This is great because all of this information is available in advance and can be used for a prediction. On the other hand, product-related columns, such PackageWeight and ScreenSize, have very small impacts on the prediction. This means that in the future I could simplify the overall process by feeding only shipping information into the training and prediction phases.

I’m happy with the analysis insights. Therefore, I decide to proceed and build a prediction model by selecting the Standard build option.

Now I can go for a walk, attend a few productive meetings, or simply spend some time with family. SageMaker Canvas is doing all of the work for me, training hundreds of models behind the scenes. It will select the best performing one, so that I can start generating accurate predictions in a couple of hours. Of course, the training duration will vary depending on the dataset size and problem type.

After about an hour and a half, the model is ready and the console lets me analyze its accuracy and the column impacts visually. I’m also happy to see that the model predicts the correct value 95.8% of the time, which is even higher than the estimated accuracy.

Optionally, I could also inspect advanced metrics such as Precision, Recall, F1 Score, and so on. These metrics help me understand how the model is performing and what kind of false positives and false negatives I can expect from this model.

From here, I could share the model into Amazon SageMaker Studio or continue using the Canvas UI to generate new predictions.

I decide to continue with the intuitive UI and select Predict. Now I can work with individual records or with a dataset for batch predictions.

When selecting Single prediction, SageMaker Canvas simplifies my life and lets me start from an existing record. I modify the column values and get immediate feedback on the prediction and the corresponding feature importance.

This quick feedback loop and intuitive UI allows me to use the ML model without having to write custom code. In case I decide to integrate the model into an automated production system, the Amazon SageMaker Studio integration lets me share the model easily with other data scientists in my team.

Generally Available Today
SageMaker Canvas is generally available in US East (Ohio), US East (N. Virginia), US West (Oregon), Europe (Frankfurt), and Europe (Ireland). You can start using it with your local datasets, as well as data already stored on Amazon S3, Amazon Redshift, or Snowflake. With just a few clicks, you’ll prepare and join your datasets, analyze estimated accuracy, verify which columns are impactful, train the best performing model, and generate new individual or batch predictions. We’re excited to hear your feedback and help you solve even more business problems with ML.

Alex

Amazon Kinesis Data Streams On-Demand – Stream Data at Scale Without Managing Capacity

Post Syndicated from Marcia Villalba original https://aws.amazon.com/blogs/aws/amazon-kinesis-data-streams-on-demand-stream-data-at-scale-without-managing-capacity/

Today we are launching Amazon Kinesis Data Streams On-demand, a new capacity mode. This capacity mode eliminates capacity provisioning and management for streaming workloads.

Kinesis Data Streams is a fully-managed, serverless service for real-time processing of streamed data at a massive scale. Kinesis Data Streams can take any amount of data, from any number of sources, and scale up and down as needed. Creating a new data stream is easy, since we announced Kinesis Data Streams in November 2013. To get started, you only need to specify the number of shards with which you must provision your stream.

Shards are the way to define capacity in Kinesis Data Streams. Each shard can ingest 1 MB/s and 1,000 records/second and egress up to 2 MB/s. You can add or remove shards of the stream using Kinesis Data Streams APIs to adjust the stream capacity according to the throughout needs of their workloads. This lets you make sure that producer and consumer applications don’t experience any throttling.

As customers adopt data streaming broadly, workloads with data traffic that can increase by millions of events in a few minutes are becoming more common. For these volatile traffic patterns, customers carefully plan capacity, monitor throughput, and in some cases develop processes that automatically change the Kinesis Data Streams stream capacity.

Kinesis Data Streams On-Demand Mode
That is why today we are announcing Kinesis Data Streams On-demand. This new capacity mode eliminates the need for provisioning and managing the capacity for streaming data. Using Kinesis Data Streams On-demand automatically scales the capacity in response to varying data traffic. Customers are charged per gigabyte of data written, read, and stored in the stream, in a pay-per-throughput fashion.

Data streams in the on-demand mode have the same high durability, high availability, low latency, security, and deep AWS integrations that Kinesis Data Streams already provides. Moreover, there are no new APIs to write or read data. All existing Kinesis Data Streams integrations work in the on-demand mode.

Kinesis Data Streams uses the partition key to distribute data across shards. That is why when using Kinesis Data Streams On-demand, you still must specify a partition key for each record to write data into a data stream, as you do today in Kinesis Data Streams using the provisioned mode. In Kinesis Data Streams On-demand, the data stream automatically adapts to handle uneven data distribution patterns. But you must be careful that no partition key exceeds a shard’s limits. If this happens, then you will receive write throttles, and then you can retry these requests.

When a new data stream is created using Kinesis Data Streams On-demand, it gets created with the default capacity of 4 MB/s and 4,000 records per second for writes. Kinesis Data Streams On-demand can automatically scale up to 200 MB/s and 200,000 records per second for writes.

Kinesis Data Streams On-demand accommodates up to double its previous peak write throughput observed in the last 30 days. As your data stream’s write throughput hits a new peak, Kinesis Data Streams automatically scales the stream’s capacity.

For example, if your data stream has a write throughput that varies between 10 MB/s and 40 MB/s, Kinesis Data Streams will make sure that you can easily burst to double the peak—80 MB/s. And, if later on that same data stream reaches a new peak of 50 MB/s, then Kinesis Data Streams will make sure that there is enough capacity to ingest 100 MB/s. However, write throttling can occur if your traffic grows more than double the previous peak in less than 15 minutes.

When to Use Kinesis Data Streams On-demand
On-demand mode is great for customers that have an unknown or variable workload, or who simply don’t want to deal with capacity management. On-demand mode works best for workloads that have even partition key distribution. For example, you run a mobile game that has variable traffic through the week or day, as customers play mostly on nights or weekends. Or, you run a streaming platform that hosts live shows, and you see a sudden increase in demand depending on the guests you have.

In addition, you can switch between on-demand and provision mode twice a day. For example, you run an e-commerce site with predictable traffic. But, starting next month, there will be many marketing campaigns launched globally. You don’t know the impact that those will have on the site traffic. Switch your Kinesis Data Streams to on-demand mode, and now you can enjoy the automated capacity planning and management for your data streams.

Get Started with Kinesis Data Streams On-demand
Create a new data stream with Kinesis Data Streams On-demand from the AWS console, AWS SDKs, AWS Command Line Interface (CLI), and AWS CloudFormation.

To create one from the console, visit the Kinesis console and Create data stream. When selecting the capacity mode, select On-demand.

Creating a data stream

At the end of the page, all of the settings for the new data stream are presented. These settings can be changed after the data stream has been created.

Data stream settings

Let’s See This in Action!
For this demo, I want to show you how the new Kinesis Data Streams capability works. This situation is best described if you at look at the following Amazon CloudWatch graphs. The green line represents the bytes ingested successfully into the stream, and the red line shows the percentage of traffic that is throttled.

First, we will start with a stream provisioned with five shards. For the first three minutes, we are sending a load of 4 MB/s. You can see that the stream can handle the load.

At the time stamp 21:19, we increase the load to 12 MB/s. Now the stream cannot handle the load, and the throttles start (the red line starts climbing up to 60 percent of request being throttled).

Increase the load on a provisioned stream

At the time stamp 21:23, we change the stream capacity from provisioned to on-demand. You can do that on-the-fly without affecting the stream. See that it takes a very short time for the stream to handle the load when converting from one capacity mode to the other.

In a few minutes (time stamp 21:24) the throttles start to drop as the stream starts scaling up. The stream capacity doubles to 10 shards first (time stamp 21:26), and the stream keeps scaling up until each shard has a load of less than 0.5 MB/s. In this way, if the stream suddenly receives double the amount of load, then it has the capacity ready to handle it.

Change to on-demand mode

At the time stamp 21:26, the load in the stream is increased to 18 MB/s. You can see the green line climbing to 350,000 records – there are no throttles, and the stream ends this demo with 40 open shards. This means that if suddenly the stream receives a load of 40 MB/s, then it could handle it with no problem.

Increase the load

Available Now!
The Amazon Kinesis Data Streams On-demand is available globally in all commercial Regions.

You can learn more about the capacity modes in the Amazon Kinesis Data Streams Developer Guide.

Marcia

Introducing Amazon Redshift Serverless – Run Analytics At Any Scale Without Having to Manage Data Warehouse Infrastructure

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/introducing-amazon-redshift-serverless-run-analytics-at-any-scale-without-having-to-manage-infrastructure/

We’re seeing the use of data analytics expanding among new audiences within organizations, for example with users like developers and line of business analysts who don’t have the expertise or the time to manage a traditional data warehouse. Also, some customers have variable workloads with unpredictable spikes, and it can be very difficult for them to constantly manage capacity.

With Amazon Redshift, you use SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes. Today, I am happy to introduce the public preview of Amazon Redshift Serverless, a new capability that makes it super easy to run analytics in the cloud with high performance at any scale. Just load your data and start querying. There is no need to set up and manage clusters. You pay for the duration in seconds when your data warehouse is in use, for example, while you are querying or loading data. There is no charge when your data warehouse is idle.

Amazon Redshift Serverless automatically provisions the right compute resources for you to get started. As your demand evolves with more concurrent users and new workloads, your data warehouse scales seamlessly and automatically to adapt to the changes. You can optionally specify the base data warehouse size to have additional control on cost and application-specific SLAs.

With the new serverless option, you can continue to query data in other AWS data stores, such as Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Aurora and Amazon Relational Database Service (RDS) databases.

Amazon Redshift Serverless is ideal when it is difficult to predict compute needs such as variable workloads, periodic workloads with idle time, and steady-state workloads with spikes. This approach is also a good fit for ad-hoc analytics needs that need to get started quickly and for test and development environments.

Let’s see how this works in practice.

Using Amazon Redshift Serverless
I go to the Amazon Redshift console and choose the new serverless option. The first time, I set up the serverless endpoint and configure networking and security.

I confirm the default settings that use all subnets in my default Amazon Virtual Private Cloud (VPC) and its default security group. Data is always encrypted, and I use the default AWS-owned key. Optionally, I can customize all settings. I can associate now or later the AWS Identity and Access Management (IAM) roles to give permissions to access other AWS resources, for example, to be able to load data from an S3 bucket. The configuration of the serverless endpoint will be shared by all my serverless data warehouses in the same AWS account and Region.

Console screenshot.

To query data, I use Amazon Redshift Query Editor V2, a new free web-based tool that we made available a few months back. The query editor provides quick access to a few sample datasets to make it easy to learn Amazon Redshift’s SQL capabilities: TPC-H, TPC-DS, and tickit, a dataset containing information on ticket sales for events.

For a quick test, I use the tickit sample dataset so I don’t need to load any data. I prepare a query to get the list of tickets sold per date, sorted to see the dates with more sales first:

SELECT caldate, sum(qtysold) as sumsold
FROM   tickit.sales, tickit.date
WHERE  sales.dateid = date.dateid 
GROUP BY caldate
ORDER BY sumsold DESC;

By using the web-based query editor, I don’t need to configure a SQL client or set up the network permissions to reach the serverless endpoint. Instead, I just write my SQL query and run it.

Console screenshot.

I am a visual person. I enable the Chart option on the right of the result table and select a bar chart.

Console screenshot.

Satisfied with the clarity of the chart, I export it as an image file. In this way, I can quickly share it or include it in a report.

Bar chart

Amazon Redshift Serverless supports all rich SQL functionality of Amazon Redshift such as semi-structured data support. I can use any JDBC/ODBC-compliant tool or the Amazon Redshift Data API to query my data. To migrate data, I can take a snapshot of an Amazon Redshift provisioned cluster and restore it as serverless. Then, I just need to update my SQL applications to use the new serverless endpoint.

Availability and Pricing
Amazon Redshift Serverless is available in public preview in the following AWS Regions: US East (N. Virginia), US West (N. California, Oregon), Europe (Frankfurt, Ireland), Asia Pacific (Tokyo).

With Amazon Redshift Serverless, you pay separately for the compute and storage you use. Compute capacity is measured in Redshift Processing Units (RPUs), and you pay for the workloads in RPU-hours with per-second billing. For storage, you pay for data stored in Amazon Redshift-managed storage and storage used for snapshots, similar to what you’d pay with a provisioned cluster using RA3 instances.

To control your costs, you can specify usage limits and define actions that Amazon Redshift automatically takes if those limits are reached. You can specify usage limits in RPU-hours and associated with a daily, weekly, or monthly duration. Setting higher usage limits can improve the overall throughput of the system, especially for workloads that need to handle high concurrency while maintaining consistently high performance.

Compute resources automatically shutdown behind the scenes when there is no activity and resume when you are loading data, or there are queries coming in. When accessing your S3 data lake via the new serverless endpoint, you do not pay for Amazon Redshift Spectrum separately. You have a unified serverless experience and pay for data lake queries also in RPU-seconds. For more information, see the Amazon Redshift pricing page.

The serverless end point is configured at the AWS account level. If you have multiple teams or projects and want to manage costs separately, you can use separate AWS accounts. You can share data between your provisioned clusters and serverless endpoint and between serverless endpoints across accounts.

To help you get practice, we provide you upfront with $500 in AWS credits to try the Amazon Redshift Serverless public preview. You get the credits when you first create a database with Amazon Redshift Serverless. These credits are used to cover your costs for compute, storage, and snapshot usage of Amazon Redshift Serverless only.

Start using Amazon Redshift Serverless today to run and scale analytics without having to provision and manage data warehouse clusters.

Danilo

AWS Lake Formation – General Availability of Cell-Level Security and Governed Tables with Automatic Compaction

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/aws-lake-formation-general-availability-of-cell-level-security-and-governed-tables-with-automatic-compaction/

A data lake can help you break down data silos and combine different types of analytics into a centralized repository. You can store all of your structured and unstructured data in this repository. However, setting up and managing data lakes involve a lot of manual, complicated, and time-consuming tasks. AWS Lake Formation makes it easy to set up a secure data lake in days instead of weeks or months.

Today, I am excited to share the general availability of some new features that simplify even further loading data, optimizing storage, and managing access to a data lake:

  • Governed Tables – A new type of Amazon Simple Storage Service (Amazon S3) tables that makes it simple and reliable to ingest and manage data at any scale. Governed tables support ACID transactions that let multiple users concurrently and reliably insert and delete data across multiple governed tables. ACID transactions also let you run queries that return consistent and up-to-date data. In case of errors in your extract, transform, and load (ETL) processes, or during an update, changes are not committed and will not be visible.
  • Storage Optimization with Automatic Compaction for governed tables – When this option is enabled, Lake Formation automatically compacts small S3 objects in your governed tables into larger objects to optimize access via analytics engines, such as Amazon Athena and Amazon Redshift Spectrum. By using automatic compaction, you don’t have to implement custom ETL jobs that read, merge, and compress data into new files, and then replace the original files.
  • Granular Access Control with Row and Cell-Level Security – You can control access to specific rows and columns in query results and within AWS Glue ETL jobs based on the identity of who is performing the action. In this way, you don’t have to create (and keep updated) subsets of your data for different roles and legislations. This works for both governed and traditional S3 tables.

Using Governed Tables, ACID Transactions, and Automatic Compaction
In the Lake Formation console, I can enable governed data access and management at table creation. Automatic compaction is enabled by default, and it can be disabled using the AWS Command Line Interface (CLI) or AWS SDKs.

Console screenshot.

Governed tables have a manifest that tracks the S3 objects that are part of the table’s data. I can use the UpdateTableObjects API to keep the manifest updated when adding new objects to the table, and I can call it using the AWS CLI and SDKs. This API is implicitly used by the AWS Glue ETL library.

Moreover, I have access to new Lake Formation APIs to start, commit, or cancel a transaction. I can use these APIs to wrap data loading, data transformation, and output consistent and up-to-date data.

Using Row and Cell-Level Security
There are many use cases where, for a table, you want to restrict access to specific columns, rows, or a combination that depends on the role of the user accessing the data. For example, a company with offices in the US, Germany, and France can create a filter for analysts based in the European Union (EU) to limit access to EU-based customers.

Console screenshot.

The filter can enforce that some columns, such as date of birth (dob) and phone, are not accessible to those analysts. Moreover, access to individual rows can be filtered by using filter expressions. You can configure row filter expressions with a SQL-compatible syntax based on the open-source PartiQL language. In this case, only rows with country equal to Germany or France (country='DE' OR country='FR') are visible.

Console screenshot.

Availability and Pricing
These new features are available today in the following AWS Regions: US East (N. Virginia), US West (Oregon), Europe (Ireland), US East (Ohio), and Asia Pacific (Tokyo).

When querying governed tables, or tables secured with row and cell-level security, you pay by the amount of data scanned (with a 10MB minimum). When using governed tables, transaction metadata is charged by the number of S3 objects tracked, and you pay for the number of transaction requests. Automatic compaction is charged based on the data processed. For more information, see the AWS Lake Formation pricing page.

While implementing these features, we introduced a new Lake Formation Storage API that is integrated with tools such as AWS Glue, Amazon Athena, Amazon Redshift Spectrum, and Amazon QuickSight. You can use this storage API directly in your applications to query tables with a SQL-like syntax (joins are not supported) and get the benefits of governed tables and cell-level security.

See the detailed blog series published during the preview to learn more:

Effective data lakes using AWS Lake Formation

Take advantage of these new features to simplify the creation and management of your data lake.

Danilo

New – Amazon EBS Snapshots Archive

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/new-amazon-ebs-snapshots-archive/

I am pleased to announce the availability of Amazon EBS Snapshots Archive, a new storage tier for the long-term retention of Amazon Elastic Block Store (EBS) snapshots of your EBS volumes.

In a nutshell, EBS is an easy-to-use high-performance block storage service for your Amazon Elastic Compute Cloud (Amazon EC2) instances. An EBS volume mounted to your EC2 instances lets you boot an operating system and store data for your most performance-demanding workloads. You may use EBS snapshots to create point-in-time copies of your volume data. The first snapshot of a volume contains all of the data written into that volume. Subsequent snapshots are incremental. Snapshots are stored on Amazon Simple Storage Service (Amazon S3), and they may be shared between AWS accounts and AWS Regions.

The ability to take frequent snapshots and easily restore volumes makes EBS snapshots an obvious choice for your data management strategy, alongside other backup options. The incremental nature of snapshots makes them cost-effective for daily and weekly backups that need immediate restores. However, you were telling us that business compliance and regulatory needs have meant that you needed to retain EBS snapshots for longer periods of time (months or years). For example, snapshots taken at the end of a project, or snapshots for test and development preserved for future project releases. The vast majority of these snapshots are taken and never read. For these snapshots, you are looking to lower your storage costs. Today, to benefit from lower storage costs, you may have written complex scripts involving temporary EC2 instances to restore snapshots, mount the corresponding volumes, and transfer the data to lower-cost storage tiers, such as Amazon Glacier.

EBS Snapshots Archive provides a low-cost storage tier to archive full, point-in-time copies of EBS Snapshots that you must retain for 90 days or more for regulatory and compliance reasons, or for future project releases. Now, you can easily archive and manage EBS Snapshots, thereby eliminating the need for custom scripts and third-party tools to manage these snapshots. This lets you move your rarely accessed snapshots to EBS Snapshots Archive to achieve up to 75% lower storage costs, and avoid licensing costs for third-party tools. Furthermore, you can retrieve an archived snapshot within 24-72 hours, and, once restored, use the snapshot to recover an EBS volume.

As per usual, let me show you how it works.

How to Get Started
I have a snapshot available in the US East (N. Virginia) Region, and I want to archive this snapshot for compliance reasons. I open the AWS Management Console, navigate to EC2, then to Snapshots. I select the snapshot I want to archive, and select the Actions menu. I select the Archive snapshot menu option.

EBS Snapshot Archive - create snapshot

I carefully read the confirmation message :-), and I select Archive snapshot.

EBS Snapshot Archive - create snapshot - confirmation

I may monitor the progress of the archive operation with the new Storage Tier tab at the bottom of the screen. After some time, depending on the size of the snapshot, the Tiering status becomes ✅ Archival completed.

EBS Snapshot Archive - create snapshot - archival completedArchived snapshots stay visible in the console. The new Storage tier column indicates the tier used for storage (Standard or Archive).

How do I Restore a Volume?
Restoring a volume from EBS Snapshots Archive is a two-step process. First, I retrieve the snapshot from EBS Snapshots Archive to its original snapshot ID, using RestoreSnapshotTier API call or the management console. It takes between 24-72 hours to retrieve the snapshot from the archive, depending on the snapshot size. Once retrieved, the snapshot appears as a regular snapshot on my account. At this stage, I hydrate the retrieved snapshot into an EBS volume using the default snapshot restore or Fast Snapshot Restore (FSR) for expedited restores, just like usual.

A CloudWatch event is generated when the snapshot is restored. You may listen to this event to avoid pulling the status with the API.

A CreateVolume API call on an archived snapshot will fail. You must restore a snapshot from archive before you use it to create a volume.

Using the AWS Management Console, I select the snapshot that I want to restore, I select the Actions menu, and then I select the Restore snapshot from archive menu option.

EBS Snapshot Archive - create snapshot - restore archive

I have the choice to restore the snapshot permanently, or just temporarily. At the end of the temporary duration, the standard tier snapshot is deleted, and only the archive is preserved.

EBS Snapshot Archive - create snapshot - restore archive - confirmation

After a while, depending on the snapshot size, the archive is restored to standard storage and may be used to recreate a volume, just like usual. I may monitor the progress of the retrieval and the lifetime for temporarily restored archives in the new Storage tier tab in the bottom half of the screen. Temporary restored snapshots may be kept for up to 180 days.

Pricing and Availability
EBS Snapshots Archive is available for you today in 17 AWS Regions. At the time of launch, it is not available in the two Regions in China, Asia Pacific (Seoul), Asia Pacific (Osaka), Canada (Central), and South America (São Paulo).

As per usual, you pay as-you-go, with no minimum or fixed fees. There are two metrics that influence EBS Snapshots Archive billing: data storage and data retrieval. We charge you $0.0125 per GB-month of stored data and $0.03 per GB retrieved. You are charged for a 90-day period at minimum. This means that if you delete a snapshot archive or permanently restore it less than 90 days after creation, then we charge for the full 90-day period. The EBS pricing page has the details.

Go ahead and start to configure your long term storage for EBS snaphots today.

— seb

New – AWS Control Tower Account Factory for Terraform

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/new-aws-control-tower-account-factory-for-terraform/

AWS Control Tower makes it easier to set up and manage a secure, multi-account AWS environment. AWS Control Tower uses AWS Organizations to create what is called a landing zone, bringing ongoing account management and governance based on our experience working with thousands of customers.

If you use AWS CloudFormation to manage your infrastructure as code, you can customize your AWS Control Tower landing zone using Customizations for AWS Control Tower, a solution that helps you deploy custom templates and policies to individual accounts and organizational units (OUs) within your organization.

But what if you use Terraform to manage your AWS infrastructure?

Today, I am happy to share the availability of AWS Control Tower Account Factory for Terraform (AFT), a new Terraform module maintained by the AWS Control Tower team that allows you to provision and customize AWS accounts through Terraform using a deployment pipeline. The source code for the development pipeline can be stored in AWS CodeCommit, GitHub, GitHub Enterprise, or BitBucket. With AFT, you can automate the creation of fully functional accounts that have access to all the resources they need to be productive. The module works with Terraform open source, Terraform Enterprise, and Terraform Cloud.

Architectural diagram.

Let’s see how this works in practice.

Using AWS Control Tower Account Factory for Terraform
First, I create a main.tf file that uses the AWS Control Tower Account Factory for Terraform (AFT) module:

module "aft" {
  source = "[email protected]:aws-ia/terraform-aws-control_tower_account_factory.git"

  # Required Parameters
  ct_management_account_id    = "123412341234"
  log_archive_account_id      = "234523452345"
  audit_account_id            = "345634563456"
  aft_management_account_id   = "456745674567"
  ct_home_region              = "us-east-1"
  tf_backend_secondary_region = "us-west-2"

  # Optional Parameters
  terraform_distribution = "oss"
  vcs_provider           = "codecommit"

  # Optional Feature Flags
  aft_feature_delete_default_vpcs_enabled = false
  aft_feature_cloudtrail_data_events      = false
  aft_feature_enterprise_support          = false
}

The first six parameters are required. As a prerequisite, I need to pass the ID of four AWS accounts in my AWS organization:

  • ct_management_account_id – AWS Control Tower management account
  • log_archive_account_id – Log Archive account
  • audit_account_id – Audit account
  • aft_management_account_id – AFT management account

Then, I have to pass two AWS Regions:

  • ct_home_region – The Region from which this module will be executed. This must be the same Region where AWS Control Tower is deployed.
  • tf_backend_secondary_region – The backend primary Region is the same as the AFT Region. This parameter defines the secondary Region to replicate to. AFT creates a backend for state tracking for its own state. It is also used for Terraform when using the open-source version.

The other parameters are optional and are set to their default value in the previous main.tf file:

  • terraform_distribution – To select between Terraform open source (default), Enterprise, or Cloud
  • vcs_provider – To choose the version control system to use between AWS CodeCommit (default), GitHub, GitHub Enterprise, or BitBucket.

These feature flags are disabled by default and can be omitted unless you want to enable them:

  • aft_feature_delete_default_vpcs_enabled – To automatically delete the default VPC for new accounts.
  • aft_feature_cloudtrail_data_events – To enable AWS CloudTrail data events for new accounts. Be aware that this option, usually required for compliance in highly regulated environments, can have an impact on your costs.
  • aft_feature_enterprise_support – To automatically enroll new accounts with Enterprise Support (if you have an Enterprise Support Plan).

First, I initialize the project and download the plugins:

terraform init

Then, I use AWS Single Sign-On to log in with the AWS Control Tower management account and start the deployment:

terraform apply

I confirm with a yes and, after some time, the deployment is complete.

Now, I use AWS SSO again to log in with the AFT management account. In the AWS CodeCommit console, I find four repositories that I can use to customize the accounts created with AFT.

Console screenshot.

These repositories are used by pipelines managed by AWS CodePipeline to automate the account creation:

  • xaft-account-request – This is where I place requests for accounts provisioned and managed by AFT.
  • aft-global-customizations – I can use this repository to customize all provisioned accounts with customer-defined resources. The resources can be created through Terraform or through Python.
  • aft-account-customizations – Here, I can customize provisioned accounts depending on the value of the account_customizations_name parameter in the aft-account-request repository. In this way, I can create different sets of customizations depending on the role the account will be used for.
  • aft-account-provisioning-customizations – This repository uses AWS Step Functions to customize the provisioning process for new accounts and simplify the integration with additional environments. State machines can use AWS Lambda functions, Amazon Elastic Container Service (Amazon ECS) or AWS Fargate tasks, custom activities hosted either on AWS or on-premises, or Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) to communicate with external applications.

Currently, these four repositories are all empty. To start, I use the code in the sources/aft-customizations-repos folder in the GitHub repo of the AFT Terraform module.

Using the example in the aft-account-request repository, I prepare a template to create a couple of AWS accounts. One of the two accounts is for a software developer.

To help software developers be productive quickly, I create a specific account customization. In the template, I set the parameter account_customizations_name equal to developer-customization.

Then, in the aft-account-customizations repository, I create a developer-customization folder where I put a Terraform template to automatically create an AWS Cloud9 EC2-based development environment for new accounts of that type. Optionally, I can extend that with my Python code, for example, to invoke internal or external APIs. Using this approach, all new accounts for software developers will have their development environment ready as they go through the delivery pipeline.

I push the changes to the main branch (first for the aft-account-customizations repository, then for the aft-account-request). This triggers the execution of the pipeline. After a few minutes, the two new accounts are ready to be used.

You can customize accounts created by AFT based on your unique requirements. For example, you can provide each account with its own specific security setup (such as IAM roles or security groups) and storage (for example, pre-configured Amazon Simple Storage Service (Amazon S3) buckets).

Availability and Pricing
AWS Control Tower Account Factory for Terraform (AFT) works in any Region where AWS Control Tower is available. There are no additional costs when using AFT. You pay for the services used by the solution. For example, when you set up AWS Control Tower, you will begin to incur costs for AWS services configured to set up your landing zone and mandatory guardrails.

When building this solution, we worked together with HashiCorp. Armon Dadgar, HashiCorp Co-Founder and CTO, told us: “Managing cloud environments with hundreds or thousands of users can be a complex and time-consuming process. Using a software delivery pipeline integrating Terraform and AWS Control Tower makes it easier to achieve consistent governance and compliance requirements across all accounts.”

The pipeline provides an account creation process that monitors when account provisioning is complete and then triggers additional Terraform modules to enhance the account with further customizations. You can configure the pipeline to use your own custom Terraform modules or pick from pre-published Terraform modules for common products and configurations.

Simplify and standardize AWS account creation using AWS Control Tower Account Factory for Terraform.

Danilo

Announcing AWS Data Exchange for APIs: Find, Subscribe to, and Use Third-party APIs with Consistent Authentication

Post Syndicated from Alex Casalboni original https://aws.amazon.com/blogs/aws/data-exchange-for-apis-find-subscribe-use-third-party-apis-consistent-authentication/

Data is at the center of many processes and products, whether it’s a large-scale dataset used to train machine learning models, a relational database, or an API-based integration. AWS Data Exchange lets you discover, subscribe to, and use hundreds of file-based datasets via Amazon Simple Storage Service (Amazon S3) offered by third parties such as Reuters, Foursquare, Change Healthcare, Vortexa, IMDb, and many more. Additionally, AWS Data Exchange for Amazon Redshift makes it even easier to ingest third-party data in your Amazon Redshift data warehouse, without any manual processing or transformation.

However, in many cases your data projects require more than static datasets because you need frequent and synchronous retrieval of small amounts of information – for example, you might need to fetch a stock price every hour. Data APIs let you answer specific questions quickly and without having to build ad-hoc data pipelines to ingest, process, and analyze bulk datasets. But each API provider has its own ease of use, SDK, documentation, and authentication mechanisms, which makes this harder than it needs to be.

Today, I’m happy to announce the general availability of AWS Data Exchange for APIs, a new capability that lets you find, subscribe to, and use third-party APIs with a consistent access using AWS SDKs, as well as consistent AWS-native authentication and governance. This simplifies the lives of developers and IT administrators who have to integrate and secure the access to multiple third-party APIs.

Now you can make RESTful or GraphQL API calls directly to AWS Data Exchange and receive synchronous responses that contain the information you need, using the AWS SDK in the programming language of your choice. We take care of integrating with the API provider, implementing proper authentication, managing the API subscription, and ensuring charges appear on your AWS bill. You can manage API access centrally with AWS Identity and Access Management (IAM).

As a data provider, you make your API discoverable by millions of AWS customers by listing it in the AWS Data Exchange catalog using an OpenAPI specification and fronting it with an Amazon API Gateway endpoint.

AWS Data Exchange for APIs in Action
First, I look for an API product in the AWS Data Exchange catalog, review its subscription terms, support information, and auto-renewal. Each API product might include multiple public or private subscription offers and periods.

I select Subscribe and a couple of minutes later I’m successfully subscribed.

Within the API product, I select an entitled data set and its latest revision.

Each API revision contains one or more API assets that correspond to a specific API endpoint and a unique Asset ARN.

AWS Data Exchange takes care of invoking API endpoints with the correct authentication.

All I need to do is check the Integration notes, which include instructions and code snippets based on the AWS Command Line Interface (CLI).

Of course, I could implement the very same API call with my favorite programming language using one of the AWS SDKs.

For example, here’s how I’d implement a simple wrapper function in Python:

import json
import urllib
import boto3

adx = boto3.client('dataexchange')

def get_api_response(path, method="GET", querystring={}, headers={}, body={}):
    return adx.send_api_asset(
        DataSetId="4b3fbabc31171662851531b8576a3411",
        RevisionId="e8e78e921af12c76499edc40f92e3082",
        AssetId="557d858c317efdfb5b6c9a2860ec4a03",
        Method=method,
        Path=path,
        QueryStringParameters=urllib.urlencode(querystring),
        RequestHeaders=urllib.urlencode(headers),
        Body=json.dumps(body),
    )

Please note that there are no hard-coded credentials in the code above because all the authorization happens via AWS Identity and Access Management (IAM).

And that’s how you make your first API call via AWS Data Exchange for APIs.

Available Today
AWS Data Exchange for APIs is generally available in all AWS Regions where AWS Data Exchange is available. We’re looking forward to helping you simplify and centralize the management and governance of third-party APIs while we take care of the undifferentiated heavy lifting for you.

Today you can start integrating third-party APIs such as Infutor, Variety Business Intelligence, IMDb, PeopleDataLabs, Neustar, Experian, Foursquare, PredictHQ, WeatherTrends International, and many more.

If you’re a developer, check out the new AWS Data Exchange for APIs documentation to learn more about subscribing and using APIs. If you’re an API provider, check out the new publishing documentation to learn more about publishing new APIs on the AWS Data Exchange catalog.

Alex

Improved, Automated Vulnerability Management for Cloud Workloads with a New Amazon Inspector

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/improved-automated-vulnerability-management-for-cloud-workloads-with-a-new-amazon-inspector/

Amazon Inspector is a service used by organizations of all sizes to automate security assessment and management at scale. Amazon Inspector helps organizations meet security and compliance requirements for workloads deployed to AWS, scanning for unintended network exposure, software vulnerabilities, and deviations from application security best practice.

Since the original launch of Amazon Inspector in 2015, vulnerability management for cloud customers has changed considerably. Over the last six years, the team delivered several new customer-requested features, including assessment reporting, support for proxy environments, and integration with Amazon CloudWatch Metrics. However, the team also recognized that there were new requirements to meet – enabling frictionless deployment at scale, support for an expanded set of resource types needing assessment, and a critical need to detect and remediate at speed. Today I’m happy to announce a new Amazon Inspector, able to meet these requirements with the following features:

  • Continual, automated assessment scans—replaces periodic, manual scanning.
  • Automated resource discovery – once enabled, the new Amazon Inspector automatically discovers all running Amazon Elastic Compute Cloud (Amazon EC2) instances and Amazon Elastic Container Registry repositories.
  • New support for container-based workloads—workloads are now assessed on both EC2 and container infrastructure.
  • Integration with AWS Organizations—allowing security and compliance teams to enable and take advantage of Amazon Inspector across all accounts in an organization.
  • Removal of the stand-alone Amazon Inspector scanning agent—assessment scanning now uses the widely deployed AWS Systems Manager agent, eliminating the need for a separate agent installation.
  • Improved risk scoring—a highly contextualized risk score is now generated for each finding by correlating Common Vulnerability and Exposures (CVE) metadata with environmental factors for resources, such as network accessibility. This makes it easier to identify the most critical vulnerabilities to address as a priority.
  • Integration with Amazon EventBridge—integrate with event management and workflow systems such as Splunk and Jira. And, you can trigger automated remediation, for example, system patching using Systems Manager or virtual machine image rebuilds using EC2 Image Builder.
  • Integration with AWS Security Hub—helping your teams to more easily identify those resources with critical vulnerabilities or deviations from security best practices.

Automatically Assessing your Workloads with Amazon Inspector
Tens of thousands of vulnerabilities exist, with new ones being discovered and made public on a regular basis. With this continually growing threat, manual assessment can lead to customers being unaware of an exposure and thus potentially vulnerable between assessments. Additionally, customers with manual processes for managing their inventories of applications resources, the deployment of stand-alone security agents on those resources, and the scheduling of periodic assessments may find the whole process to be a costly and time-consuming exercise. That’s before they have to then sift through the mass of assessment findings to determine the most critical issues to address.

With the new Amazon Inspector, all you need to do is enable the service. It will auto-discover and start continual assessment of your EC2 and your Amazon Elastic Container Registry-based container workloads to evaluate your security posture, even as the underlying resources change.

EC2 instances are discovered and assessed for unintended exposure to external networks and software vulnerabilities using the Systems Manager agent, already included by default in images provided by AWS for instance management, automated patching, and more. Container-based workloads are assessed as the images are pushed to Amazon Elastic Container Registry. Without needing additional software or agents, container images and EC2 instances are assessed in near real time when an event occurs.

Automated assessment is driven by changes in workload configuration and newly published vulnerabilities to ensure resources are only assessed when needed. The new Amazon Inspector collects events from over 50 vulnerability intelligence sources, including CVE, the National Vulnerability Database (NVD), and MITRE. Images that may be affected by a newly identified entry, for example, a new CVE notification, will be automatically rescanned. Image rescanning is enabled for 30 days from the date they are pushed to the registry. You can also enable an option to only scan on image push and not subsequently perform rescans.

Summary page for Amazon Inspector

Selecting either Accounts, Instances, or Repositories from your Dashboard page takes you to a detail summary for the selected resource. Below, I’m viewing summary data for EC2 instances across a couple of accounts.

Viewing instances scanned by Amazon Inspector across accounts

If vulnerabilities are found, you receive actionable assessment findings in a report. Starting today, these findings are summarized with enhanced risk scoring and improved resource detail to help you prioritize the most at-risk resources needing to be addressed. Also new today, the Amazon Inspector console has been redesigned to surface all findings and recommendations for remediation.

Vulnerabilities in container images are also sent to Amazon Elastic Container Registry to be summarized for the owner. And, as I noted earlier, new integrations with AWS Security Hub and Amazon EventBridge allow findings to be sent downstream for additional visibility and remediation by automated workflows. For example, automation can be created to isolate instances, trigger system patching, software image rebuilds, and more. The availability of multiple integration points makes it easier for security and application teams to collaborate to manage remediation. Below, I’m viewing findings from Amazon Inspector in the AWS Security Hub console.

Viewing findings from Amazon Inspector in the Security Hub console

Assessments can result in hundreds of thousands, or more, findings that need to be filtered and sifted to determine the most critical to action. Also available today, organizations can determine which of the findings they consider to be acceptable and mark those findings for temporary or permanent suppression. This helps reduce the volume of alerts, further assisting with prioritization and automated remediation. Suppression filters can be set from several screens. Rules specify one or more filters, such as Severity, that will cause findings that match the filters to be removed from display. When defining rules, a list is shown of the findings that will be suppressed, helping you fine-tune the filter values to match your specific needs.

Setting up suppression rules for Amazon Inspector findings

I mentioned earlier that the new Amazon Inspector implements a contextualized risk assessment score for findings. The screenshot below shows an example of Amazon Inspector‘s risk assessment score, compared to a generic Common Vulnerability Scoring System (CVSS) score. Contextual risk assessment takes into account additional factors such as accessibility to the internet and ease of exploitability to make the score more meaningful. In the image below, Amazon Inspector‘s risk assessment score is lower than the CVSS score because the attack vector requires network access. Amazon Inspector knows that the vulnerability identified in the GNOME Glib will be difficult to exploit because in this resource, there is no network access, and therefore it lowered the risk score.

Risk assessment score

Start a Free Trial with Amazon Inspector Today
The new Amazon Inspector is available now in the US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Hong Kong), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Milan), Europe (Paris), Europe (Stockholm), Middle East (Bahrain), and South America (São Paulo) Regions.

Amazon Inspector offers a free 15-day trial, so you can put it to work to see how Amazon Inspector can help your security and compliance teams reduce operational complexity and cost associated with managing resource inventories, stand-alone security agents, and repetitive manual assessments.

— Steve

Announcing AWS Well-Architected Custom Lenses: Extend the Well-Architected Framework with Your Internal Best Practices

Post Syndicated from Alex Casalboni original https://aws.amazon.com/blogs/aws/well-architected-custom-lenses-internal-best-practices/

We launched the AWS Well-Architected Framework back in 2015 to help you review workloads against architectural best practices, and across pillars such as operational excellence, security, reliability, performance efficiency, and cost optimization. In 2017, we extended the framework with the concept of “lenses” to optimize specific workload types such as the Serverless Lens, the SaaS Lens, and the Foundational Technical Review (FTR) Lens for APN Partners. In 2018, we launched the AWS Well-Architected Tool, a self-service tool designed to help you review AWS workloads at any time, without the need for an AWS Solutions Architect.

Today, I’m happy to announce the general availability of AWS Well-Architected Custom Lenses, a new feature of the AWS Well-Architected Tool that lets you bring your own best practices to complement the existing framework based on your industry, operational plans, and internal processes. Custom Lenses provide a consolidated view and a consistent way to measure and improve your workloads on AWS without relying on external spreadsheets or third-party systems.

In addition to AWS Well-Architected Lenses, now you can create and share custom lenses and include them in your workload reviews, ultimately tailoring the review to your organizational needs. For example, you could define a custom lens to review your workloads against PCI compliance, SOC 2 compliance, or other national or industry regulations. As an AWS Partner, you might include ad-hoc best practices in your custom lenses when reviewing workloads with customers from different industries and segments, ultimately making the review process easier, faster, and more comprehensive.

How to Define a new Custom Lens
You author a new custom lens by editing a JSON preset template, where you define questions, choices, helpful resources, improvement plans, and risk rules.

Here’s how it works: download the template from the AWS Well-Architected Tool, work on it locally, and then re-upload it.

The JSON structure is composed of multiple pillars. Each pillar might contain multiple questions, each with its own choices and risk rules.

Your JSON file will look like this:

{
    "schemaVersion": "2021-11-01",
    "name": "My Test Lens",
    "description": "This is a description of my test lens.",
    "pillars": [
        {
            "id": "pillar_red",
            "name": "Red Pillar",
            "questions": [
                {
                    "id": "pillar_1_q1",
                    "title": "How do you get started with this pillar?",
                    "description": "Optional description.",
                    "choices": [
                        {
                            "id": "choice1",
                            "title": "Best practice #1",
                            "helpfulResource": {
                                "displayText": "This is helpful text for the first choice.",
                                "url": "https://aws.amazon.com"
                            },
                            "improvementPlan": {
                                "displayText": "This is text that will be shown for improvement of this choice."
                            }
                        },
                        {
                            "id": "choice2",
                            "title": "Best practice #2",
                            ...
                        }
                    ],
                    "riskRules": [
                        {
                            "condition": "choice1 && choice2",
                            "risk": "NO_RISK"
                        },
                        {
                            "condition": "choice1 && !choice2",
                            "risk": "MEDIUM_RISK"
                        },
                        {
                            "condition": "default",
                            "risk": "HIGH_RISK"
                        }
                    ]
                }
            ]
            ...
        },
        ...
    ]
}

Once you’re ready to submit your JSON file, proceed with the upload.

And don’t worry about making it perfect on the first try. You’ll be able to improve it and add new versions.

AWS Well-Architected Custom Lenses in Action
You find the list of custom lenses and their latest version in the new Custom Lenses section.

Each custom lens has an owner and can be shared with multiple AWS accounts too.

Before using this new custom lens in a workload review, you’ll need to publish it and assign it a version.

Select Publish lens and provide a version name such as 1.0.

Now you can create a new workload review and apply both AWS-owned lenses and your own custom lenses, in addition to the main framework.

During the workload review, you will go through each pillar and questions of the custom lens, using the same user interface provided by the AWS Well-Architected Tool.

Last but not least, you can share your custom lens with other AWS Identity and Access Management (IAM) principals such as AWS accounts, IAM users, and IAM roles.

Available Today at No Charge
Custom Lenses are available today in all AWS Regions where the AWS Well-Architected Tool is available, at no cost. You can define up to five custom lenses and share them across AWS Accounts, in addition to the existing Well-Architected Framework and AWS-owned Lenses.

Check out the technical documentation here.

We’re looking forward to hearing your feedback and iterating quickly to improve the authoring and sharing experience based on your needs.

Alex

Announcing Pull Through Cache Repositories for Amazon Elastic Container Registry

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/announcing-pull-through-cache-repositories-for-amazon-elastic-container-registry/

Organizations, development teams, and individual developers who have chosen to use containers to host their applications may prefer, or perhaps are required, to source all images from Amazon Elastic Container Registry to take advantage of its high availability and security. To satisfy those requirements, customers have needed to take on the burden of manually pulling images from public registries into their private Amazon Elastic Container Registry repositories, and then keeping them in sync. This adds operational complexity and maintenance costs, thereby impacting developer productivity. Additionally, some registries may have limitations or restrictions on how frequently images can be downloaded. When reached, those limitations then begin impacting developers and the release velocity of their business, due to build errors when image pulls are throttled, or even rejected.

Today, we have announced pull through cache repository support in Amazon Elastic Container Registry, for publicly accessible registries that do not require authentication. Pull through cache repositories offer developers the improved performance, security, and availability of Amazon Elastic Container Registry for container images that they source from public registries. Images in pull through cache repositories are automatically kept in sync with the upstream public registries, thereby eliminating the manual work of pulling images and periodically updating.

Pull through cache repositories provide the benefits of the built-in security capabilities in Amazon Elastic Container Registry, such as AWS PrivateLink enabling you to keep all of the network traffic private, image scanning to detect vulnerabilities, encryption with AWS Key Management Service (KMS) keys, cross-region replication, and lifecycle policies. When enabled, cross-region replication is designed to automatically distribute updated images to additional Regions. All you need to do is update the pull URL so that the image is downloaded from the relevant Region.

When consuming images from pull through cache repositories, download throttling is also no longer a problem for developers, as well as the build and deployment infrastructure that supports their applications. While Amazon Elastic Container Registry is designed to automatically keep the cache repository in sync, you can also manually sync a repository at any time. And, if you wish, the automatic sync can be turned off.

Getting Started with Amazon Elastic Container Registry Pull Through Cache Repositories
Setting up pull through cache repositories is a simple process. For the following example, I’m using Amazon Elastic Container Registry Public in the South America (São Paulo) Region as my upstream registry.

First, I must modify my private registry’s settings to add a rule that references the upstream, publicly accessible registry (multiple rules can be set if I need additional upstream registries). In the Amazon Elastic Container Registry console, I begin by selecting Private registry, and then select Edit in the Pull through cache panel to change settings. This takes me to the Pull through cache configuration page, where I select Add rule.

On the Create pull through cache rule page, I choose the upstream registry, which is ECR Public in this example. I also must set a namespace that I’ll use when referring to images in my pull commands. For this example, I’ll accept the suggested namespace, ecr-public.

Configuring ECR Public as the upstream registry

Selecting Save takes me back to the Pull through cache configuration page where my newly configured rule is listed. Now, I’m ready to utilize the cache repository when pulling images.

Newly configured rule for an upstream registry

To reference an image, I must specify the namespace that I chose in the pull URL, using the URL format <accountId>.dkr.ecr.<region>.amazonaws.com/<namespace>/<sourcerepo>:<tag>. When images are pulled, the cache repository associated with the namespace is checked for the image. In my case, the cache repository doesn’t exist yet, but I don’t have to create it myself. The image is fetched from the upstream repository in the public registry associated with the namespace, and then stored in a new cache repository that is created for me automatically.

In the command prompt session below, I first authenticate with my registry, and then pull an Amazon Linux 2 image from Amazon Elastic Container Registry Public into the cache:

C:\ aws ecr get-login-password --region sa-east-1 | docker login --username AWS --password-stdin 111122223333.dkr.ecr.sa-east-1.amazonaws.com/ecr-public
Login Succeeded
C:\ docker pull 111122223333.dkr.ecr.sa-east-1.amazonaws.com/ecr-public/amazonlinux/amazonlinux:latest
latest: Pulling from ecr-public/amazonlinux/amazonlinux
e11e8d46e102: Pull complete
Digest: sha256:916dbbb288948b54c94b5b9f0769085aa601d4468d099e90d8a7da5cfa551b50
Status: Downloaded newer image for 111122223333.dkr.ecr.sa-east-1.amazonaws.com/ecr-public/amazonlinux/amazonlinux:latest
111122223333.dkr.ecr.us-west-2.amazonaws.com/ecr-public/amazonlinux/amazonlinux:latest

In my Amazon Elastic Container Registry console, a check of the Repositories page shows that a new private repository has been created containing the image I pulled, together with an indication that a pull through cache is active.

Pulled image in the cache repository

Working with images and the pull through cache repository is just as straightforward in Dockerfiles. All I need do is reference the image I need using the namespace in the pull URL. If the image is not in the cache repository, then it will be pulled and stored there for me. Cached images are checked once per 24 hours to verify if the cached image is the latest version, with the timer based off the last pull time of the cached image.

Start using Pull through Cache Repositories Today
Pull through cache repositories for Amazon Elastic Container Registry are available for you to take advantage of today in all commercial AWS Regions. There is no charge for using pull through cache repositories, only standard Amazon Elastic Container Registry pricing for storage and data transfer charges applies. You can find more details on pricing at the Amazon Elastic Container Registry pricing page. Learn more about pull through cache repositories in the Amazon Elastic Container Registry User Guide, and get started today.

— Steve

Introducing Amazon Braket Hybrid Jobs – Set Up, Monitor, and Efficiently Run Hybrid Quantum-Classical Workloads

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/introducing-amazon-braket-hybrid-jobs-set-up-monitor-and-efficiently-run-hybrid-quantum-classical-workloads/

I find quantum computing fascinating! At its simplest level, it extends the concept of bits, that have 0 or 1 values, with quantum bits, or qubits, that can have a combination of two different (quantum) states.

Two characteristics make qubits really interesting:

  • When you look at the value of a qubit, you get only one of the two possible states with a probability that depends on how its own states are combined.
  • Multiple qubits can be “connected” together (this is called quantum entanglement) so that by changing the state of one, even just by reading its value, you alter the states of the others.

These characteristics come from low-level properties described by quantum mechanics, a fundamental theory in physics that provides a description of the physical properties of nature at atomic and subatomic scales. Luckily, we don’t need a degree in quantum mechanics to use quantum computing in the same way we don’t need to be expert in semiconductors to use an ordinary computer.

Using qubits, researchers are designing new algorithms that have the potential to be much faster than what classical computers can achieve. To help speed up scientific research and software development for quantum computing, we introduced Amazon Braket at re:Invent 2019. A fully managed quantum computing service, Amazon Braket allows you to build, test, and run quantum algorithms on simulators and quantum computers.

Hybrid Algorithms and Quantum Processing Units (QPUs)
Quantum algorithms, which would be transformational in many different areas, require the execution of hundreds of thousands to millions of quantum gates. Unfortunately, the current generation of QPUs suffer from noise, creating errors that limit operations to only a few hundreds or thousands of gates before the errors take over.

To help solve this, we can take inspiration from machine learning: instead of using fixed quantum circuits, the logic that implements the algorithm, we let the algorithm “learn” by adjusting the parameters that tune the circuit to have a better chance of solving a given problem by adapting to the noise in a particular device (think of them as “self-learning quantum algorithms”).

This is similar to computer vision: instead of hand-crafting the features to distinguish a dog from a cat (which is notoriously difficult for a computer), machine learning algorithms “learn” the right features by iteratively adjusting parameters of a neural network.

A rapidly emerging area of research in quantum computing uses QPUs, the processors used by quantum computers, in the same way as GPUs are used in machine learning: Quantum circuits are parameterized, initialized with some values, and then run on the QPU. Like the weights in a neural network, these parameters are then iteratively adjusted based on the results of the computation. These so-called hybrid algorithms rely on rapid, iterative computations between classical computers and QPUs.

Architectural diagram.

To run hybrid algorithms, you need to manually set up a classical infrastructure, install the required software, and manage the interaction between your quantum and classical compute processes for the duration of your hybrid algorithm. You then need to build custom monitoring solutions to visualize the progress of your algorithm to make sure it converges to the solution as expected or intervene if necessary to adjust the parameters of the algorithm.

Another big challenge is that QPUs are shared, inelastic resources, and you compete with others for access. This can slow down the execution of your algorithm. A single large workload from another customer can bring the algorithm to a halt, potentially extending your total runtime for hours. This is not only inconvenient but also impacts the quality of the results because today’s QPUs need periodic re-calibration, which can invalidate the progress of a hybrid algorithm. In the worst case, the algorithm fails, wasting budget and time.

Introducing Amazon Braket Hybrid Jobs
Today, I am happy to introduce Amazon Braket Hybrid Jobs, a new capability of Amazon Braket that simplifies the process of setting up, monitoring, and efficiently executing hybrid quantum-classical algorithms. Jobs are fully managed so you can avoid extensive infrastructure and software management and confidently execute your algorithms quickly and predictably, with on-demand priority access to QPUs.

When you create a job, Amazon Braket spins up the job instance (providing a CPU environment based on an Amazon Elastic Compute Cloud (Amazon EC2) instance), executes the algorithm (using quantum hardware or simulators), and releases the resources once the job is completed so that you only pay for what you use. You can also define custom metrics for algorithms, which are automatically logged by Amazon CloudWatch and displayed in near real-time in the Amazon Braket console as the algorithm runs. This provides you with live insights into how your algorithm is progressing, creating the opportunity to adjust your algorithm as necessary and innovate more quickly.

Architectural diagram.

To run hybrid algorithms as jobs, you can define your algorithm using the Amazon Braket SDK or with PennyLane, an open-source library for hybrid quantum computing. Let’s see how that works in practice with a couple of examples.

Using Amazon Braket Hybrid Jobs
Before building a trainable quantum algorithm, let’s get started by running a series of fixed quantum operations, what we’ll refer to as quantum tasks. I use Python and the Amazon Braket SDK to define a circuit that constructs what is called a bell state, a state which has a fifty-fifty chance of resolving to each of two states. It’s the quantum computing equivalent of tossing a coin.

Here’s the content of the algorithm_script.py file:

import os

from braket.aws import AwsDevice
from braket.circuits import Circuit
from braket.jobs import save_job_result


def start_here():

    print("Test job started!")

    device = AwsDevice(os.environ["AMZN_BRAKET_DEVICE_ARN"])

    results = []
    
    bell = Circuit().h(0).cnot(0, 1)
    for count in range(5):
        task = device.run(bell, shots=100)
        print(task.result().measurement_counts)
        results.append(task.result().measurement_counts)

    save_job_result({ "measurement_counts": results })
    
    print("Test job completed!")

This script uses the environment variable AMZN_BRAKET_DEVICE_ARN to instantiate the device that I select when creating the job.

Quantum computing is probabilistic. For this reason, circuits need to be evaluated multiple times to get accurate results. A single run is called a shot. The higher the number of shots, the better the accuracy of the result. In this case, the circuit is run for 100 shots.

I use the save_job_result function to store the results of my job so that I can analyze them at the end.

In the Amazon Braket console, I choose Jobs on the left panel and then Create job. To start, I give the job a name.

Console screenshot.

Then, I pass the file with the algorithm. The CPU component of the hybrid algorithm runs in a container, and I can choose which container image to use. For example, I can use a pre-built container image that includes software my algorithm depends on, such as PennyLane, TensorFlow, or PyTorch, or bring my own custom image. I select the Base container image because I don’t have external dependencies.

I leave all other settings to their default value. In this way, I use the SV1 simulator, rather than quantum hardware, to run the quantum tasks.

After some time, the job has completed, and I follow the link to the Amazon Simple Storage Service (Amazon S3) console to download the result. As expected, for each of the five tasks, the results show that the proportion of the 00 and 11 states is roughly 50:50. The proportions vary slightly because of the probabilistic nature of quantum computing.

{
    "braketSchemaHeader": {
        "name": "braket.jobs_data.persisted_job_data",
        "version": "1"
    },
    "dataDictionary": {
        "measurement_counts": [
            {
                "00": 51,
                "11": 49
            },
            {
                "00": 44,
                "11": 56
            },
            {
                "11": 51,
                "00": 49
            },
            {
                "00": 56,
                "11": 44
            },
            {
                "00": 49,
                "11": 51
            }
        ]
    },
    "dataFormat": "plaintext"
}

This example is quite basic because I am not running any classical logic other than initiating tasks. To see the real value, let’s see how it works with a hybrid algorithm where we tweak the parameters of the quantum circuit iteratively from task to task.

Using Amazon Braket Hybrid Jobs with Hybrid Algorithms
For a more advanced example, I use a well-known example of an actual hybrid algorithm, called the quantum approximate optimization algorithm (QAOA), included in the examples provided by Amazon Braket when creating a notebook from the Braket console. QAOA is a quantum algorithm that produces approximate solutions for combinatorial optimization problems. You can also find the example in this GitHub repo.

In this case, I am using QAOA to solve the Max-Cut problem: when partitioning nodes of a graph in two, what is the maximum number of edges connecting nodes between the two parts? For example, in the figure below, there are six nodes connected by eight edges. The thick yellow line partitions the nodes into two sets by crossing six edges.

In the QAOA example, the tuning of parameters that are used to run the successive rounds of quantum tasks is optimized in a classical computing environment (such as an EC2 instance) using tools like TensorFlow or PyTorch. In one of the notebook cells, I can choose which interface to use to tune the parameters as well as the other hyperparameters in a similar way to what I’d do for machine learning training.

Braket jobs then coordinates running the classical and quantum computing parts of the algorithm and the exchange of parameters and results between them. I can just sit back and relax as I watch my algorithm converge, ready to retrieve my results from S3, as before, for deeper analysis.

Running Hybrid Algorithms in Local Mode
To test and debug hybrid algorithms quickly, the Amazon Braket SDK can run jobs in local mode. With local mode, Braket jobs are run locally on your machine (for example, your laptop). In this way, you can get fast feedback and iterate quickly during the development of your algorithms.

To run a job in local mode, you just need to replace AwsQuantumJob with LocalQuantumJob. Note that AwsQuantumJob is imported from braket.aws , while LocalQuantumJob is imported from braket.jobs.local.

Availability and Pricing
Amazon Braket Hybrid Jobs are available today in all AWS Regions where Amazon Braket is available. For more information, see the AWS Regional Services List.

With Amazon Braket Hybrid Jobs, you only pay for the resources you use. There is no need to deploy, configure, and manage classical infrastructure, making it easy to experiment and improve algorithms iteratively. For more information, see the Amazon Braket pricing page.

Instead of relying on theoretical studies, you can start to use quantum computers as the primary tool to understand and improve hybrid algorithms and test their applicability for industry and research use cases. In this way, you can focus on your research and not deal with setting up and coordinating these different compute resources for your experiments.

During the development of this new capability, we talked with customers and partners to understand their needs. “As application developers, Braket Hybrid Jobs gives us the opportunity to explore the potential of hybrid variational algorithms with our customers,” says Vic Putz head of engineering at QCWare. “We are excited to extend our integration with Amazon Braket and the ability to run our own proprietary algorithms libraries in custom containers means we can innovate quickly in a secure environment. The operational maturity of Amazon Braket and the convenience of priority access to different types of quantum hardware means we can build this new capability into our stack with confidence.”

Simplify running hybrid quantum-classical workloads with Amazon Braket Hybrid Jobs.

Danilo