Tag Archives: AWS Snowball Edge

Using and Managing Security Groups on AWS Snowball Edge devices

Post Syndicated from Macey Neff original https://aws.amazon.com/blogs/compute/using-and-managing-security-groups-on-aws-snowball-edge-devices/

This blog post is written by Jared Novotny & Tareq Rajabi, Specialist Hybrid Edge Solution Architects. 

The AWS Snow family of products are purpose-built devices that allow petabyte-scale movement of data from on-premises locations to AWS Regions. Snow devices also enable customers to run Amazon Elastic Compute Cloud (Amazon EC2) instances with Amazon Elastic Block Storage (Amazon EBS), and Amazon Simple Storage Service (Amazon S3) in edge locations.

Security groups are used to protect EC2 instances by controlling ingress and egress traffic. Once a security group is created and associated with an instance, customers can add ingress and egress rules to control data flow. Just like the default VPC in a region, there is a default security group on Snow devices. A default security group is applied when an instance is launched and no other security group is specified.  This default security group in a region allows all inbound traffic from network interfaces and instances that are assigned to the same security group, and allows and all outbound traffic. On Snowball Edge, the default security group allows all inbound and outbound traffic.

In this post, we will review the tools and commands required to create, manage and use security groups on the Snowball Edge device.

Some things to keep in mind:

  1. AWS Snowball Edge is limited to 50 security groups.
  2. An instance will only have one security group, but each group can have a total of 120 rules. This is comprised of 60 inbound and 60 outbound rules.
  3. Security groups can only have allow statements to allow network traffic.
  4. Deny statements aren’t allowed.
  5. Some commands in the Snowball Edge client (AWS CLI) don’t provide an output.
  6. AWS CLI commands can use the name or the security group ID.

Prerequisites and tools

Customers must place an order for Snowball Edge from their AWS Console to be able to run the following AWS CLI commands and configure security groups to protect their EC2 instances.

The AWS Snowball Edge client is a standalone terminal application that customers can run on their local servers and workstations to manage and operate their Snowball Edge devices. It supports Windows, Mac, and Linux systems.

AWS OpsHub is a graphical user interface that you can use to manage your AWS Snowball devices. Furthermore, it’s the easiest tool to use to unlock Snowball Edge devices. It can also be used to configure the device, launch instances, manage storage, and provide monitoring.

Customers can download and install the Snowball Edge client and AWS OpsHub from AWS Snowball resources.

Getting Started

To get started, when a Snow device arrives at a customer site, the customer must unlock the device and launch an EC2 instance. This can be done via AWS OpsHub or the AWS Snowball Edge Client. AWS Snow Family of devices support both Virtual Network Interfaces (VNI) and Direct Network interfaces (DNI), customers should review the types of interfaces before deciding which one is best for their use case. Note that security groups are only supported with VNIs, so that is what was used in this post. A post explaining how to use these interfaces should be reviewed before proceeding.

Viewing security group information

Once the AWS Snowball Edge is unlocked, configured, and has an EC2 instance running, we can dig deeper into using security groups to act as a virtual firewall and control incoming and outgoing traffic.

Although the AWS OpsHub tool provides various functionalities for compute and storage operations, it can only be used to view the name of the security group associated to an instance in a Snowball Edge device:

view the name of the security group associated to an instance in a Snowball Edge device

Every other interaction with security groups must be through the AWS CLI.

The following command shows how to easily read the outputs describing the protocols, sources, and destinations. This particular command will show information about the default security group, which allows all inbound and outbound traffic on EC2 instances running on the Snowball Edge.

In the following sections we review the most common commands with examples and outputs.

View (all) existing security groups:

aws ec2 describe-security-groups --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

{
    "SecurityGroups": [
        {
            "Description": "default security group",
            "GroupName": "default",
            "IpPermissions": [
                {
                    "IpProtocol": "-1",
                    "IpRanges": [
                        {
                            "CidrIp": "0.0.0.0/0"
                        }
                    ]
                }
            ],
            "GroupId": "s.sg-8ec664a23666db719",
            "IpPermissionsEgress": [
                {
                    "IpProtocol": "-1",
                    "IpRanges": [
                        {
                            "CidrIp": "0.0.0.0/0"
                        }
                    ]
                }
            ]
        }
    ]
}

Create new security group:

aws ec2 create-security-group --group-name allow-ssh--description "allow only ssh inbound" --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

The output returns a GroupId:

{  "GroupId": "s.sg-8f25ee27cee870b4a" }

Add port 22 ingress to security group:

aws ec2 authorize-security-group-ingress --group-ids.sg-8f25ee27cee870b4a --protocol tcp --port 22 --cidr 10.100.10.0/24 --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

{    "Return": true }

Note that if you’re using the default security group, then the outbound rule is still to allow all traffic.

Revoke port 22 ingress rule from security group

aws ec2 revoke-security-group-ingress --group-ids.sg-8f25ee27cee870b4a --ip-permissions IpProtocol=tcp,FromPort=22,ToPort=22, IpRanges=[{CidrIp=10.100.10.0/24}] --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

{ "Return": true }

Revoke default egress rule:

aws ec2 revoke-security-group-egress --group-ids.sg-8f25ee27cee870b4a  --ip-permissions IpProtocol="-1",IpRanges=[{CidrIp=0.0.0.0/0}] --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

{ "Return": true }

Note that this rule will remove all outbound ephemeral ports.

Add default outbound rule (revoked above):

aws ec2 authorize-security-group-egress --group-id s.sg-8f25ee27cee870b4a --ip-permissions IpProtocol="-1", IpRanges=[{CidrIp=0.0.0.0/0}] --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

{  "Return": true }

Changing an instance’s existing security group:

aws ec2 modify-instance-attribute --instance-id s.i-852971d05144e1d63 --groups s.sg-8f25ee27cee870b4a --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

Note that this command produces no output. We can verify that it worked with the “aws ec2 describe-instances” command. See the example as follows (command output simplified):

aws ec2 describe-instances --instance-id s.i-852971d05144e1d63 --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge


    "Reservations": [{
            "Instances": [{
                    "InstanceId": "s.i-852971d05144e1d63",
                    "InstanceType": "sbe-c.2xlarge",
                    "LaunchTime": "2022-06-27T14:58:30.167000+00:00",
                    "PrivateIpAddress": "34.223.14.193",
                    "PublicIpAddress": "10.100.10.60",
                    "SecurityGroups": [
                        {
                            "GroupName": "allow-ssh",
                            "GroupId": "s.sg-8f25ee27cee870b4a"
                        }      ], }  ] }

Changing and instance’s security group back to default:

aws ec2 modify-instance-attribute --instance-ids.i-852971d05144e1d63 --groups s.sg-8ec664a23666db719 --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

Note that this command produces no output. You can verify that it worked with the “aws ec2 describe-instances” command. See the example as follows:

aws ec2 describe-instances –instance-ids.i-852971d05144e1d63 –endpoint Https://MySnowIPAddress:8008 –profile SnowballEdge

{
    "Reservations": [
        {  "Instances": [ {
                    "AmiLaunchIndex": 0,
                    "ImageId": "s.ami-8b0223704ca8f08b2",
                    "InstanceId": "s.i-852971d05144e1d63",
                    "InstanceType": "sbe-c.2xlarge",
                    "LaunchTime": "2022-06-27T14:58:30.167000+00:00",
                    "PrivateIpAddress": "34.223.14.193",
                    "PublicIpAddress": "10.100.10.60",
                             "SecurityGroups": [
                        {
                            "GroupName": "default",
                            "GroupId": "s.sg-8ec664a23666db719" ] }

Delete security group:

aws ec2 delete-security-group --group-ids.sg-8f25ee27cee870b4a --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

Sample walkthrough to add a SSH Security Group

As an example, assume a single EC2 instance “A” running on a Snowball Edge device. By default, all traffic is allowed to EC2 instance “A”. As per the following diagram, we want to tighten security and allow only the management PC to SSH to the instance.

1. Create an SSH security group:

aws ec2 create-security-group --group-name MySshGroup--description “ssh access” --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

2. This will return a “GroupId” as an output:

{   "GroupId": "s.sg-8a420242d86dbbb89" }

3. After the creation of the security group, we must allow port 22 ingress from the management PC’s IP:

aws ec2 authorize-security-group-ingress --group-name MySshGroup -- protocol tcp --port 22 -- cidr 192.168.26.193/32 --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

4. Verify that the security group has been created:

aws ec2 describe-security-groups ––group-name MySshGroup –endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

{
	“SecurityGroups”:   [
		{
			“Description”: “SG for web servers”,
			“GroupName”: :MySshGroup”,
			“IpPermissinos”:  [
				{ “FromPort”: 22,
			 “IpProtocol”: “tcp”,
			 “IpRanges”: [
			{
				“CidrIp”: “192.168.26.193.32/32”
						} ],
					“ToPort”:  22 }],}

5. After the security group has been created, we must associate it with the instance:

aws ec2 modify-instance-attribute –-instance-id s.i-8f7ab16867ffe23d4 –-groups s.sg-8a420242d86dbbb89 --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

6. Optionally, we can delete the Security Group after it is no longer required:

aws ec2 delete-security-group --group-id s.sg-8a420242d86dbbb89 --endpoint Http://MySnowIPAddress:8008 --profile SnowballEdge

Note that for the above association, the instance ID is an output of the “aws ec2 describe-instances” command, while the security group ID is an output of the “describe-security-groups” command (or the “GroupId” returned by the console in Step 2 above).

Conclusion

This post addressed the most common commands used to create and manage security groups with the AWS Snowball Edge device. We explored the prerequisites, tools, and commands used to view, create, and modify security groups to ensure the EC2 instances deployed on AWS Snowball Edge are restricted to authorized users. We concluded with a simple walkthrough of how to restrict access to an EC2 instance over SSH from a single IP address. If you would like to learn more about the Snowball Edge product, there are several resources available on the AWS Snow Family site.

New – Snowball Edge Storage Optimized Devices with More Storage and Bandwidth

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-snowball-edge-storage-optimized-devices-with-more-storage-and-bandwidth/

AWS Snow Family family devices are used to cost-effectively move data to the cloud and to process data at the edge. The enhanced Snowball Edge Storage Optimized devices are designed for your petabyte-scale data migration projects, with 210 terabytes of NVMe storage and the ability to transfer up to 1.5 gigabytes of data per second. The devices also include several connectivity options: 10GBASE-T, SFP48, and QSFP28.

Large Data Migration
In order to make your migration as smooth and efficient as possible, we now have a well-defined Large Data Migration program. As part of this program, we will work with you to make sure that your site is able to support rapid data transfer, and to set up a proof-of-concept migration. If necessary, we will also recommend services and solutions from our AWS Migration Competency Partners. After successful completion of the proof-of-concept you will be familiar with the Snow migration process, and you will be ready to order devices using the process outlined below.

You can make use of the Large Data Migration program by contacting AWS Sales Support.

Ordering Devices
While you can order and manage devices individually, you can save time and reduce complexity by using a large data migration plan. Let’s walk through the process of creating one. I open the AWS Snow Family Console and click Create your large data migration plan:

I enter a name for my migration plan (MediaMigrationPlan), and select or enter the shipping address of my data center:

Then I specify the amount of data that I plan to migrate, and the number of devices that I want to use concurrently (taking into account space, power, bandwidth, and logistics within my data center):

When everything looks good I click Create data migration plan to proceed and my plan becomes active:

I can review the Monitoring section my my plan to see how my migration is going (these are simply Amazon CloudWatch metrics and I can add them to a dashboard, set alarms, and so forth):

The Jobs section includes a recommended job ordering schedule that takes the maximum number of concurrent devices into account:

When I am ready to start transferring data, I visit the Jobs ordered tab and create a Snow job:

As the devices arrive, I connect them to my network and copy data to them via S3 (read Managing AWS Storage) or NFS (read Using NFS File Shares to Manage File Storage), then return it to AWS for ingestion!

Things to Know
Here are a couple of fun facts about this enhanced device:

Regions – Snowball Edge Storage Optimized Devices with 210 TB of storage are available in the US East (N. Virginia) and US West (Oregon) AWS Regions.

Pricing – You pay for the use of the device and for data transfer in and out of AWS, with on-demand and committed upfront pricing available. To learn more about pricing for Snowball Edge Storage Optimized 210 TB devices contact your AWS account team or AWS Sales Support.

— Jeff;

Amazon S3 Compatible Storage on AWS Snowball Edge Compute Optimized Devices Now Generally Available

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/amazon-s3-compatible-storage-on-aws-snowball-edge-compute-optimized-devices-now-generally-available/

We have added a collection of purpose-built services to the AWS Snow Family for customers, such as Snowball Edge in 2016 and Snowcone in 2020. These services run compute intensive workloads and stores data in edge locations with denied, disrupted, intermittent, or limited network connectivity and for transferring large amounts of data from on-premises and rugged or mobile environments.

Each new service is optimized for space- or weight-constrained environments, portability, and flexible networking options. For example, Snowball Edge devices have three options for device configurations. AWS Snowball Edge Compute Optimized provides a suitcase-sized, secure, and rugged device that customers can deploy in rugged and tactical edge locations to run their compute applications. Customers modernize their edge applications in the cloud use AWS compute services and storage services such as Amazon Simple Storage Service (Amazon S3), and then deploy these applications on Snow devices at the edge.

We heard from customers that they also needed access to local object store to run applications at the edge, such as 5G mobile core and real-time data analytics, to process end-user transactions, and they had limited storage infrastructure availability in these environments. Although the Amazon S3 Adapter for Snowball enables the basic storage and retrieval of objects on a Snow device, customers wanted access to a broader set of Amazon S3 APIs, including flexibility at scale, local bucket management, object tagging, and S3 event notifications.

Today, we’re announcing the general availability of Amazon S3 compatible storage on Snow for our Snowball Edge Compute Optimized devices. This makes it easy for you to store data and run applications with local S3 buckets that require low latency processing at the edge.

With Amazon S3 compatible storage on Snow, you can use an expanded set of Amazon S3 APIs to easily build applications on AWS and deploy them on Snowball Edge Compute Optimized devices. This eliminates the need to re-architect applications for each deployment. You can manage applications requiring Amazon S3 compatible storage across the cloud, on-premises, and at the edge in connected and disconnected environments with a consistent experience.

Moreover, you can use AWS OpsHub, a graphical user interface, to manage your Snow Family services and Amazon S3 compatible storage on the devices at the edge or remotely from a central location. You can also use Amazon S3 SDK or AWS Command Line Interface (AWS CLI) to create and manage S3 buckets, get S3 event notifications using MQTT, and local service notifications using SMTP, just as you do in AWS Regions.

With Amazon S3 compatible storage on Snow, we are now able to address various use cases in limited network environments, giving customers secure, durable local object storage. For example, customers in the intelligence community and in industrial IoT deploy applications such as video analytics in rugged and mobile edge locations.

Getting Started with S3 Compatible Storage on Snowball Edge Compute Optimized
To order new Amazon S3 enabled Snowball Edge devices, create a job in the AWS Snow Family console. You can replace an existing Snow device or cluster with new replacement devices that support S3 compatible storage.

In Step 1 – Job type, input your job name and choose Local compute and storage only. In Step 2 – Compute and storage, choose your preferred Snowball Edge Compute Optimized device.

Select Amazon S3 compatible storage, a new option for S3 compatible storage. The current S3 Adapter solution is on deprecation path, and we recommend migrating workloads to use Amazon S3 compatible storage on Snow.

When you select Amazon S3 compatible storage, you can configure Amazon S3 compatible storage capacity for a single device or for a cluster. The Amazon S3 storage capacity depends on the quantity and type of Snowball Edge device.

  • For single-device deployment, you can provision granular Amazon S3 capacity up to a maximum of 31 TB on a Snowball Edge Compute Optimized device.
  • For a cluster setup, all storage capacity on a device is allocated to Amazon S3 compatible storage on Snow. You can provision a maximum of 500 TB on a 16 node cluster of Snowball Edge Compute Optimized devices.

When you provide all necessary job details and create your job, you can see the status of the delivery of your device in the job status section.

Manage S3 Compatible Storage on Snow with OpsHub
Once your device arrives at your site, power it on, and connect it to your network. To manage your device, download, install, and launch the OpsHub application in your laptop. After installation, you can unlock the device and start managing it and using supported AWS services locally.

OpsHub provides a dashboard that summarizes key metrics, such as storage capacity and active instances on your device. It also provides a selection of AWS services that are supported on the Snow Family devices.

Log in to OpsHub, then choose Manage Storage. This takes you to the Amazon S3 compatible storage on Snow landing page.

For Start service setup type, choose Simple if your network uses dynamic host configuration protocol (DHCP). With this option, the virtual network interface cards (VNICs) are created automatically on each device when you start the service. When your network uses static IP addresses, you need to create VNICs for each device manually, so choose the Advanced option.

Once the service starts, you’ll see its status is active with a list of endpoints. The following example shows the service activated in a single device:

Choose Create bucket if you want the new S3 bucket in your device. Otherwise, you can upload files to your selected bucket. New uploaded objects have destination URLs such as s3-snow://test123/test_file with the unique bucket name in the device or cluster.

You can also use the bucket lifecycle rule to define when to trigger object deletion based on age or date. Choose Create lifecycle rule in the Management tab to add a new lifecycle rule.

You can select either Delete objects or Delete incomplete multipart uploads as a rule action. Configure the rule trigger that schedules deletion based on a specific date or object’s age. In this example, I set two days to delete objects after being uploaded.

You can also use the Amazon S3 SDK/CLI for all API operations supported by S3 for Snowball Edge. To learn more, see API Operations Supported on Amazon S3 for Snowball Edge in the AWS documentation.

Things to know
Keep these things in mind regarding additional features and considerations when you use Amazon S3 compatible storage on Snow:

  • Capacity: If you fully utilize Amazon S3 capacity on your device or cluster, your write (PUT) requests return an insufficient capacity error. Read (GET) operations continue to function normally. To monitor the available Amazon S3 capacity, you can use the OpsHub S3 on the Snow page or use the describe-service CLI command. Upon detecting insufficient capacity on the Snow device or cluster, you must free up space by deleting data or transferring data to an S3 bucket in the Region or another on-premises device.
  • Resiliency: Amazon S3 compatible storage on Snow stores data redundantly across multiple disks on each Snow device and multiple devices in your cluster, with built-in protection against correlated hardware failures. In the event of a disk or device failure within the quorum range, Amazon S3 compatible storage on Snow continues to operate until hardware is replaced. Additionally, Amazon S3 compatible storage on Snow continuously scrubs data on the device to make sure of data integrity and recover any corrupted data. For workloads that require local storage, the best practice is to back up your data to further protect your data stored on Snow devices.
  • Notifications: Amazon S3 compatible storage on Snow continuously monitors the health status of the device or cluster. Background processes respond to data inconsistencies and temporary failures to heal and recover data to make sure of resiliency. In the case of nonrecoverable hardware failures, Amazon S3 compatible storage on Snow can continue operations and provides proactive notifications through emails, prompting you to work with AWS to replace failed devices. For connected devices, you have the option to enable the “Remote Monitoring” feature, which will allow AWS to monitor service health online and proactively notify you of any service issues.
  • Security: Amazon S3 compatible storage on Snow supports encryption using server-side encryption with Amazon S3 managed encryption keys (SSE-S3) or customer-provided keys (SSE-C) and authentication and authorization using Snow IAM actions namespace (s3:*) to provide you with distinct controls for data stored on your Snow devices. Amazon S3 compatible storage on Snow doesn’t support object-level access control list and bucket policies. Amazon S3 compatible storage on Snow defaults to Bucket Owner is Object Owner, making sure that the bucket owner has control over objects in the bucket.

Now Available
Amazon S3 compatible storage on Snow is now generally available for AWS Snowball Edge Compute Optimized devices in all AWS Commercial and GovCloud Regions where AWS Snow is available.

To learn more, see the AWS Snowball Edge Developer Guide and send feedback to AWS re:Post for AWS Snowball or through your usual AWS support contacts.

– Channy

AWS Week in Review – October 24, 2022

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-week-in-review-october-24-2022/

Last week, we announced plans to launch the AWS Asia Pacific (Bangkok) Region, which will become our third AWS Region in Southeast Asia. This Region will have three Availability Zones and will give AWS customers in Thailand the ability to run workloads and store data that must remain in-country.

In the Works – AWS Region in Thailand
With this big news, AWS announced a 190 billion baht (US 5 billion dollars) investment to drive Thailand’s digital future over the next 15 years. It includes capital expenditures on the construction of data centers, operational expenses related to ongoing utilities and facility costs, and the purchase of goods and services from Regional businesses.

Since we first opened an office in Bangkok in 2015, AWS has launched 10 Amazon CloudFront edge locations, a highly secure and programmable content delivery network (CDN) in Bangkok. In 2020, we launched AWS Outposts, a family of fully managed solutions delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience in Thailand. This year, we also plan the upcoming launch of an AWS Local Zone in Bangkok, which will enable customers to deliver applications that require single-digit millisecond latency to end users in Thailand.

Photo courtesy of Conor McNamara, Managing Director, ASEAN at AWS

The new AWS Region in Thailand is also part of our broader, multifaceted investment in the country, covering our local team, partners, skills, and the localization of services, including Amazon Transcribe, Amazon Translate, and Amazon Connect.

Many Thailand customers have chosen AWS to run their workloads to accelerate innovation, increase agility, and drive cost savings, such as 2C2P, CP All Plc., Digital Economy Promotion Agency, Energy Response Co. Ltd. (ENRES), PTT Global Public Company Limited (PTT), Siam Cement Group (SCG), Sukhothai Thammathirat Open University, The Stock Exchange of Thailand, Papyrus Studio, and more.

For example, Dr. Werner Vogels, CTO of Amazon.com, introduced the story of Papyrus Studio, a large film studio and one of the first customers in Thailand.

“Customer stories like Papyrus Studio inspire us at AWS. The cloud can allow a small company to rapidly scale and compete globally. It also provides new opportunities to create, innovate, and identify business opportunities that just aren’t possible with conventional infrastructure.”

For more information on how to enable AWS and get support in Thailand, contact our AWS Thailand team.

Last Week’s Launches
My favorite news of last week was to launch dark mode as a beta feature in the AWS Management Console. In Unified Settings, you can choose between three settings for visual mode: Browser default, Light, and Dark. Browser default applies the default dark or light setting of the browser, dark applies the new built-in dark mode, and light maintains the current look and feel of the AWS console. Choose your favorite!

Here are some launches that caught my eye for web, mobile, and IoT application developers:

New AWS Amplify Library for Swift – We announce the general availability of Amplify Library for Swift (previously Amplify iOS). Developers can use Amplify Library for Swift via the Swift Package Manager to build apps for iOS and macOS (currently in beta) platforms with Auth, Storage, Geo, and more features.

The Amplify Library for Swift is open source on GitHub, and we deeply appreciate the feedback we have gotten from the community. To learn more, see Introducing the AWS Amplify Library for Swift in the AWS Front-End Web & Mobile Blog or Amplify Library for Swift documentation.

New Amazon IVS Chat SDKs – Amazon Interactive Video Service (Amazon IVS) now provides SDKs for stream chat with support for web, Android, and iOS. The Amazon IVS stream chat SDKs support common functions for chat room resource management, sending and receiving messages, and managing chat room participants.

Amazon IVS is a managed, live-video streaming service using the broadcast SDKs or standard streaming software such as Open Broadcaster Software (OBS). The service provides cross-platform player SDKs for playback of Amazon IVS streams you need to make low-latency live video available to any viewer around the world. Also, it offers Chat Client Messaging SDK. For more information, see Getting Started with Amazon IVS Chat in the AWS documentation.

New AWS Parameters and Secrets Lambda Extension – This is new extension for AWS Lambda developers to retrieve parameters from AWS Systems Manager Parameter Store and secrets from AWS Secrets Manager. Lambda function developers can leverage this extension to improve their application performance as it decreases the latency and the cost of retrieving parameters and secrets.

Previously, you had to initialize either the core library of a service or the entire service SDK inside a Lambda function for retrieving secrets and parameters. Now you can simply use the extension. To learn more, see AWS Systems Manager Parameter Store documentation and AWS Secrets Manager documentation.

New FreeRTOS Long Term Support Version – We announce the second release of FreeRTOS Long Term Support (LTS) – FreeRTOS 202210.00 LTS. FreeRTOS LTS offers a more stable foundation than standard releases as manufacturers deploy and later update devices in the field. This release includes new and upgraded libraries such as AWS IoT Fleet Provisioning, Cellular LTE-M Interface, coreMQTT, and FreeRTOS-Plus-TCP.

All libraries included in this FreeRTOS LTS version will receive security and critical bug fixes until October 2024. With an LTS release, you can continue to maintain your existing FreeRTOS code base and avoid any potential disruptions resulting from FreeRTOS version upgrades. To learn more, see the FreeRTOS announcement.

Here is some news on performance improvement and increasing capacity:

Up to 10X Improving Amazon Aurora Snapshot Exporting Speed – Amazon Aurora MySQL-Compatible Edition for MySQL 5.7 and 8.0 now speed up to 10x faster snapshot exports to Amazon S3. The performance improvement is automatically applied to all types of database snapshot exports, including manual snapshots, automated system snapshots, and snapshots created by the AWS Backup service. For more information, see Exporting DB cluster snapshot data to Amazon S3 in the Amazon Aurora documentation.

3X Increasing Amazon RDS Read Capacity – Amazon Relational Database Service (RDS) for MySQL, MariaDB, and PostgreSQL now supports 15 read replicas per instance, including up to 5 cross-Region read replicas, delivering up to 3x the previous read capacity. For more information, see Working with read replicas in the Amazon RDS documentation.

2X Increasing AWS Snowball Edge Compute Capacity – The AWS Snowball Edge Compute Optimized device doubled the compute capacity up to 104 vCPUs, doubled the memory capacity up to 416GB RAM, and is now fully SSD with 28TB NVMe storage. The updated device is ideal when you need dense compute resources to run complex workloads such as machine learning inference or video analytics at the rugged, mobile edge such as trucks, aircraft or ships.  You can get started by ordering a Snowball Edge device on the AWS Snow Family console.

2X Increasing Amazon SQS FIFO Default Quota – Amazon Simple Queue Service (SQS) announces the increase of default quota up to 6,000 transactions per second per API action. It is double the previous 3,000 throughput quota for a high throughput mode for FIFO (first in, first out) queues in all AWS Regions where Amazon SQS FIFO queue is available. For a detailed breakdown of default throughput quotas per Region, see Quotas related to messages in the Amazon SQS documentation.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS News
Here are some other news items that you may find interesting:

22 New or Updated Open Datasets on AWS – We released 22 new or updated datasets, including Amazonia-1 imagery, Bitcoin and Ethereum data, and elevation data over the Arctic and Antarctica. The full list of publicly available datasets is on the Registry of Open Data on AWS and is now also discoverable on AWS Data Exchange.

Sustainability with AWS Partners (ft. AWS On Air) – This episode covers a broad discipline of environmental, social, and governance (ESG) issues across all regions, organization types, and industries. AWS Sustainability & Climate Tech provides a comprehensive portfolio of AWS Partner solutions built on AWS that address climate change events and the United Nation’s Sustainable Development Goals (SDG).

AWS Open Source News and Updates #131 – This newsletter covers latest open-source projects such as Amazon EMR Toolkit for VS Code, a VS Code Extension to make it easier to develop Spark jobs on EMR and AWS CDK For Discourse, sample codes that demonstrates how to create a full environment for Discourse, etc. Remember to check out the Open source at AWS keep up to date with all our activity in open source by following us on @AWSOpen.

Upcoming AWS Events
Check your calendars and sign up for these AWS events:

AWS re:Invent 2022 Attendee Guide – Browse re:Invent 2022 attendee guides, curated by AWS Heroes, AWS industry teams, and AWS Partners. Each guide contains recommended sessions, tips and tricks for building your agenda, and other useful resources. Also, seat reservations for all sessions are now open for all re:Invent attendees. You can still register for AWS re:Invent either offline or online.

AWS AI/ML Innovation Day on October 25 – Join us for this year’s AWS AI/ML Innovation Day, where you’ll hear from Bratin Saha and other leaders in the field about the great strides AI/ML has made in the past and the promises awaiting us in the future.

AWS Container Day at Kubecon 2022 on October 25–28 – Come join us at KubeCon + CloudNativeCon North America 2022, where we’ll be hosting AWS Container Day Featuring Kubernetes on October 25 and educational sessions at our booth on October 26–28. Throughout the event, our sessions focus on security, cost optimization, GitOps/multi-cluster management, hybrid and edge compute, and more.

You can browse all upcoming in-person, and virtual events.

That’s all for this week. Check back next Monday for another Week in Review!

— Channy

This post is part of our Week in Review series. Check back each week for a quick roundup of interesting news and announcements from AWS!

New – Offline Tape Migration Using AWS Snowball Edge

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-offline-tape-migration-using-aws-snowball-edge/

Over the years, we have given you a succession of increasingly powerful tools to help you migrate your data to the AWS Cloud. Starting with AWS Import/Export back in 2009, followed by Snowball in 2015, Snowmobile and Snowball Edge in 2016, and Snowcone in 2020, each new device has given you additional features to simplify and expedite the migration process. All of the devices are designed to operate in environments that suffer from network constraints such as limited bandwidth, high connections costs, or high latency.

Offline Tape Migration
Today, we are taking another step forward by making it easier for you to migrate data stored offline on physical tapes. You can get rid of your large and expensive storage facility, send your tape robots out to pasture, and eliminate all of the time & effort involved in moving archived data to new formats and mediums every few years, all while retaining your existing tape-centric backup & recovery utilities and workflows.

This launch brings a tape migration capability to AWS Snowball Edge devices, and allows you to migrate up to 80 TB of data per device, making it suitable for your petabyte-scale migration efforts. Tapes can be stored in the Amazon S3 Glacier Flexible Retrieval or Amazon S3 Glacier Deep Archive storage classes, and then accessed from on-premises and cloud-based backup and recovery utilities.

Back in 2013 I showed you how to Create a Virtual Tape Library Using the AWS Storage Gateway. Today’s launch builds on that capability in two different ways. First, you create a Virtual Tape Library (VTL) on a Snowball Edge and copy your physical tapes to it. Second, after your tapes are in the cloud, you create a VTL on a Storage Gateway and use it to access your virtual tapes.

Getting Started
To get started, I open the Snow Family Console and create a new job. Then I select Import virtual tapes into AWS Storage Gateway and click Next:

Then I go through the remainder of the ordering sequence (enter my shipping address, name my job, choose a KMS key, and set up notification preferences), and place my order. I can track the status of the job in the console:

When my device arrives I tell the somewhat perplexed delivery person about data transfer, carry it down to my basement office, and ask Luna to check it out:

Back in the Snow Family console, I download the manifest file and copy the unlock code:

I connect the Snowball Edge to my “corporate” network:

Then I install AWS OpsHub for Snow Family on my laptop, power on the Snowball Edge, and wait for it to obtain & display an IP address:

I launch OpsHub, sign in, and accept the default name for my device:

I confirm that OpsHub has access to my device, and that the device is unlocked:

I view the list of services running on the device, and note that Tape Gateway is not running:

Before I start Tape Gateway, I create a Virtual Network Interface (VNI):

And then I start the Tape Gateway service on the Snow device:

Now that the service is running on the device, I am ready to create the Storage Gateway. I click Open Storage Gateway console from within OpsHub:

I select Snowball Edge as my host platform:

Then I give my gateway a name (MyTapeGateway), select my backup application (Veeam Backup & Replication in this case), and click Activate Gateway:

Then I configure CloudWatch logging:

And finally, I review the settings and click Finish to activate my new gateway:

The activation process takes a few minutes, just enough time to take Luna for a quick walk. When I return, the console shows that the gateway is activated and running, and I am all set:

Creating Tapes
The next step is to create some virtual tapes. I click Create tapes and enter the requested information, including the pool (Deep Archive or Glacier), and click Create tapes:

The next step is to copy data from my physical tapes to the Snowball Edge. I don’t have a data center and I don’t have any tapes, so I can’t show you how to do this part. The data is stored on the device, and my Internet connection is used only for management traffic between the Snowball Edge device and AWS. To learn more about this part of the process, check out our new animated explainer.

After I have copied the desired tapes to the device, I prepare it for shipment to AWS. I make sure that all of the virtual tapes in the Storage Gateway Console have the status In Transit to VTS (Virtual Tape Shelf), and then I power down the device.

The display on the device updates to show the shipping address, and I wait for the shipping company to pick up the device.

When the device arrives at AWS, the virtual tapes are imported, stored in the S3 storage class associated with the pool that I chose earlier, and can be accessed by retrieving them using an online tape gateway. The gateway can be deployed as a virtual machine or a hardware appliance.

Now Available
You can use AWS Snowball Edge for offline tape migration in the US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Europe (Ireland), Europe (Frankfurt), Europe (London), Asia Pacific (Sydney) Regions. Start migrating petabytes of your physical tape data to AWS, today!

— Jeff;

Migrating to an Amazon Redshift Cloud Data Warehouse from Microsoft APS

Post Syndicated from Sudarshan Roy original https://aws.amazon.com/blogs/architecture/migrating-to-an-amazon-redshift-cloud-data-warehouse-from-microsoft-aps/

Before cloud data warehouses (CDWs), many organizations used hyper-converged infrastructure (HCI) for data analytics. HCIs pack storage, compute, networking, and management capabilities into a single “box” that you can plug into your data centers. However, because of its legacy architecture, an HCI is limited in how much it can scale storage and compute and continue to perform well and be cost-effective. Using an HCI can impact your business’s agility because you need to plan in advance, follow traditional purchase models, and maintain unused capacity and its associated costs. Additionally, HCIs are often proprietary and do not offer the same portability, customization, and integration options as with open-standards-based systems. Because of their proprietary nature, migrating HCIs to a CDW can present technical hurdles, which can impact your ability to realize the full potential of your data.

One of these hurdles includes using AWS Schema Conversion Tool (AWS SCT). AWS SCT is used to migrate data warehouses, and it supports several conversions. However, when you migrate Microsoft’s Analytics Platform System (APS) SQL Server Parallel Data Warehouse (PDW) platform using only AWS SCT, it results in connection errors due to the lack of server-side cursor support in Microsoft APS. In this blog post, we show you three approaches that use AWS SCT combined with other AWS services to migrate Microsoft’s Analytics Platform System (APS) SQL Server Parallel Data Warehouse (PDW) HCI platform to Amazon Redshift. These solutions will help you overcome elasticity, scalability, and agility constraints associated with proprietary HCI analytics platforms and future proof your analytics investment.

AWS Schema Conversion Tool

Though using AWS SCT only will result in server-side cursor errors, you can pair it with other AWS services to migrate your data warehouses to AWS. AWS SCT converts source database schema and code objects, including views, stored procedures, and functions, to be compatible with a target database. It highlights objects that require manual intervention. You can also scan your application source code for embedded SQL statements as part of database-schema conversion project. During this process, AWS SCT optimizes cloud-native code by converting legacy Oracle and SQL Server functions to their equivalent AWS service. This helps you modernize applications simultaneously. Once conversion is complete, AWS SCT can also migrate data.

Figure 1 shows a standard AWS SCT implementation architecture.

AWS SCT migration approach

Figure 1. AWS SCT migration approach

The next section shows you how to pair AWS SCT with other AWS services to migrate a Microsoft APS PDW to Amazon Redshift CDW. We prove you a base approach and two extensions to use for data warehouses with larger datasets and longer release outage windows.

Migration approach using SQL Server on Amazon EC2

The base approach uses Amazon Elastic Compute Cloud (Amazon EC2) to host a SQL Server in a symmetric multi-processing (SMP) architecture that is supported by AWS SCT, as opposed to Microsoft’s APS PDW’s massively parallel processing (MPP) architecture. By changing the warehouse’s architecture from MPP to SMP and using AWS SCT, you’ll avoid server-side cursor support errors.

Here’s how you’ll set up the base approach (Figure 2):

  1. Set up the SMP SQL Server on Amazon EC2 and AWS SCT in your AWS account.
  2. Set up Microsoft tools, including SQL Server Data Tools (SSDT), remote table copy, and SQL Server Integration Services (SSIS).
  3. Use the Application Diagnostic Utility (ADU) and SSDT to connect and extract table lists, indexes, table definitions, view definitions, and stored procedures.
  4. Generate data description languages (DDLs) using step 3 outputs.
  5. Apply these DDLs to the SMP SQL Server on Amazon EC2.
  6. Run AWS SCT against the SMP SQL database to begin migrating schema and data to Amazon Redshift.
  7. Extract data using remote table copy from source, which copies data into the SMP SQL Server.
  8. Load this data into Amazon Redshift using AWS SCT or AWS Database Migration Service (AWS DMS).
  9. Use SSIS to load delta data from source to the SMP SQL Server on Amazon EC2.
Base approach using SMP SQL Server on Amazon EC2

Figure 2. Base approach using SMP SQL Server on Amazon EC2

Extending the base approach

The base approach overcomes server-side issues you would have during a direct migration. However, many organizations host terabytes (TB) of data. To migrate such a large dataset, you’ll need to adjust your approach.

The following sections extend the base approach. They still use the base approach to convert the schema and procedures, but the dataset is handled via separate processes.

Extension 1: AWS Snowball Edge

Note: AWS Snowball Edge is a Region-specific service. Verify that the service is available in your Region before planning your migration. See Regional Table to verify availability.

Snowball Edge lets you transfer large datasets to the cloud at faster-than-network speeds. Each Snowball Edge device can hold up to 100 TB and uses 256-bit encryption and an industry-standard Trusted Platform Module to ensure security and full chain-of-custody for your data. Furthermore, higher volumes can be transferred by clustering 5–10 devices for increased durability and storage.

Extension 1 enhances the base approach to allow you to transfer large datasets (Figure 3) while simultaneously setting up an SMP SQL Server on Amazon EC2 for delta transfers. Here’s how you’ll set it up:

  1. Once Snowball Edge is enabled in the on-premises environment, it allows data transfer via network file system (NFS) endpoints. The device can then be used with standard Microsoft tools like SSIS, remote table copy, ADU, and SSDT.
  2. While the device is being shipped back to an AWS facility, you’ll set up an SMP SQL Server database on Amazon EC2 to replicate the base approach.
  3. After your data is converted, you’ll apply a converted schema to Amazon Redshift.
  4. Once the Snowball Edge arrives at the AWS facility, data is transferred to the SMP SQL Server database.
  5. You’ll subsequently run schema conversions and initial and delta loads per the base approach.
Solution extension that uses Snowball Edge for large datasets

Figure 3. Solution extension that uses Snowball Edge for large datasets

Note: Where sequence numbers overlap in the diagram is a suggestion to possible parallel execution

Extension 1 transfers initial load and later applies delta load. This adds time to the project because of longer cutover release schedules. Additionally, you’ll need to plan for multiple separate outages, Snowball lead times, and release management timelines.

Note that not all analytics systems are classified as business-critical systems, so they can withstand a longer outage, typically 1-2 days. This gives you an opportunity to use AWS DataSync as an additional extension to complete initial and delta load in a single release window.

Extension 2: AWS DataSync

DataSync speeds up data transfer between on-premises environments and AWS. It uses a purpose-built network protocol and a parallel, multi-threaded architecture to accelerate your transfers.

Figure 4 shows the solution extension, which works as follows:

  1. Create SMP MS SQL Server on EC2 and the DDL, as shown in the base approach.
  2. Deploy DataSync agent(s) in your on-premises environment.
  3. Provision and mount an NFS volume on the source analytics platform and DataSync agent(s).
  4. Define a DataSync transfer task after the agents are registered.
  5. Extract initial load from source onto the NFS mount that will be uploaded to Amazon Simple Storage Service (Amazon S3).
  6. Load data extracts into the SMP SQL Server on Amazon EC2 instance (created using base approach).
  7. Run delta loads per base approach, or continue using solution extension for delta loads.
Solution extension that uses DataSync for large datasets

Figure 4. Solution extension that uses DataSync for large datasets

Note: where sequence numbers overlap in the diagram is a suggestion to possible parallel execution

Transfer rates for DataSync depend on the amount of data, I/O, and network bandwidth available. A single DataSync agent can fully utilize a 10 gigabit per second (Gbps) AWS Direct Connect link to copy data from on-premises to AWS. As such, depending on initial load size, transfer window calculations must be done prior to finalizing transfer windows.

Conclusion

The approach and its extensions mentioned in this blog post provide mechanisms to migrate your Microsoft APS workloads to an Amazon Redshift CDW. They enable elasticity, scalability, and agility for your workload to future proof your analytics investment.

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