Tag Archives: storage

Introducing Amazon EBS Volume Clones across AWS accounts

Post Syndicated from Channy Yun (윤석찬) original https://aws.amazon.com/blogs/aws/introducing-amazon-ebs-volume-clones-across-aws-accounts/

Last year, we introduced Volume Clones of Amazon Elastic Block Store (Amazon EBS), a new capability that lets you create instant point-in-time copies of your EBS volumes within the same Availability Zone.

Today, we are extending Volume Clones with cross-account copy, so you can create copies of your EBS volumes into other AWS accounts and optionally re-encrypt them with an AWS Key Management Service (AWS KMS) key in the target account.

With this new feature, you can use your latest application data to develop, test, and experiment in a secondary environment, while protecting and isolating the information in the production environment. For example, you can create copies of a production environment to refresh test and development environments set up in separate accounts with the desired EBS encryption.

Copy EBS volumes across AWS accounts in action
To create a copy of an EBS volume across accounts, the owners of the volume can first grant the target account access to their volume in AWS Resource Access Manager (RAM), which provides a way to share resources across AWS accounts or within an AWS Organization. Then, from the target account, they can locate the volume they have access to create a copy of it.

To get started, choose Share volume for the volume you want to share with the target account in the Amazon EBS console.

Share the volume with other AWS accounts by adding it to existing resource shares, or create a new resource share in the AWS RAM console. For more details, refer to the AWS RAM User Guide.

You can now see confirmation that the volume has been shared in the Volume sharing tab of the volume detail page.

A target account must accept the resource share on the RAM console.

Once they accept the resource share, they can see the volumes in the EBS volume page of the target account. Choose Copy volume for any shared volume.

To share and copy EBS volumes across AWS accounts programmatically, including calling APIs and searching documentation, try the AWS MCP Server and plugins with your preferred AI coding tool. To learn more, visit the Amazon EBS User Guide.

Things to know
Let me share some important technical details that I think you’ll find useful.

  • Encryption: You can share unencrypted volumes and volumes encrypted with a customer managed key (CMK). Volumes encrypted with the default AWS managed key (AMK) cannot be shared. When copying a shared volume encrypted with a CMK, the CMK must also be shared with the target account. You can specify a different CMK to re-encrypt the copy in the target account.
  • Monitoring: You can monitor SharedVolumeCopyInitiated through AWS CloudTrail event in your account. You will also receive events in Amazon EventBridge at the start of the copy operation when the state of the copied volume is initializing, and at the end of the operation when the state of the copied volume changes to completed. You can see the shared volume ID, consuming account ID, and event time.
  • Pricing: Once a copy is initiated, you’ll pay a one-time fee based on your volume size, charged to the account where the copy will reside. There’s no cost for sharing EBS volumes through AWS RAM. The copied volume will incur regular EBS volume charges upon creation.
  • Availability Zone: The volume copy must be created in the same Availability Zone as the source volume. Use Availability Zone IDs (such as use1-az1) to identify the same physical location across accounts.

Now available
Cross-account volume clones for Amazon EBS are available in all AWS Regions that support Amazon EBS Volume Clones. For Regional availability and a future roadmap, visit the AWS Capabilities by Region.

Give this feature a try in the Amazon EC2 console today and send feedback to AWS re:Post for Amazon EBS or through your usual AWS Support contacts.

— Channy

160-bay NVMe SSD 4U Server Shown at FMS 2026 Supermicro ASG-4116S-NU160R

Post Syndicated from Eric Smith original https://www.servethehome.com/160-bay-nvme-ssd-4u-server-shown-at-fms-2026-supermicro-asg-4116s-nu160r/

On the FMS 2026 show floor, we saw a massive 160-bay NVMe storage server (Supermicro ASG-4116S-NU160R) that can deliver crazy storage density

The post 160-bay NVMe SSD 4U Server Shown at FMS 2026 Supermicro ASG-4116S-NU160R appeared first on ServeTheHome.

Spotted at Computex 2026: Micron’s First PCIe Gen6 Data Center SSD, the 9650

Post Syndicated from Ryan Smith original https://www.servethehome.com/spotted-at-computex-2026-microns-first-pcie-gen6-data-center-ssd-the-9650/

Ahead of the launch of PCIe Gen6 CPUs, Micron was at Computex 2026 showing off their first PCIe Gen6 SSD for data centers, the 9650

The post Spotted at Computex 2026: Micron’s First PCIe Gen6 Data Center SSD, the 9650 appeared first on ServeTheHome.

Silicon Motion Shows Off Enterprise SM8008 and SM8388 SSD Controllers at Computex 2026

Post Syndicated from Ryan Smith original https://www.servethehome.com/silicon-motion-shows-off-enterprise-sm8008-sm8388-ssd-controllers-at-computex-2026/

At Computex 2026, Silicon Motion was showcasing its SM8388 and SM8008 SSD controllers for enterprise drives

The post Silicon Motion Shows Off Enterprise SM8008 and SM8388 SSD Controllers at Computex 2026 appeared first on ServeTheHome.

Asustor Showcases Flashstor 12 Pro Gen3 & Flashstor 6 Gen3 All-Flash NASes at Computex 2026

Post Syndicated from Ryan Smith original https://www.servethehome.com/asustor-showcases-flashstor-12-pro-gen3-flashstor-6-gen3-all-flash-nases-at-computex-2026/

For Computex 2026, Asustor had their upcoming third-generation Flashstor NASes on display. With support for either 6 or 12 M.2 SSDs, these all-flash NASes continue to be an interesting offering

The post Asustor Showcases Flashstor 12 Pro Gen3 & Flashstor 6 Gen3 All-Flash NASes at Computex 2026 appeared first on ServeTheHome.

This is the Storage of Spaceborne Computer 4 Bringing AI Compute to the Moon

Post Syndicated from Patrick Kennedy original https://www.servethehome.com/this-is-the-storage-kioxia-hpe-spaceborne-computer-4-bringing-ai-compute-to-the-moon/

We found a HPE Spaceborne Computer 4 compute module bringing AI inference to the moon later this year and the storage may surprise you

The post This is the Storage of Spaceborne Computer 4 Bringing AI Compute to the Moon appeared first on ServeTheHome.

Amazon S3 annotations: attach rich, queryable context directly to your objects

Post Syndicated from Daniel Abib original https://aws.amazon.com/blogs/aws/amazon-s3-annotations-attach-rich-queryable-context-directly-to-your-objects/

Today, we’re announcing a new metadata capability for Amazon Simple Storage Service (Amazon S3) called annotations, enabling you to attach rich, large-scale business context directly to your objects. You can store up to 1,000 named annotations per object, each up to 1 MB in size, totaling up to 1 GB per object, in flexible formats like JSON, XML, YAML, or plain text. You can modify or delete an annotation at any time, without re-writing your objects, making it easy to keep your object context current.

Organizations are building AI agents and autonomous workflows that need to find, understand, and act on data without human intervention. To support these agentic workflows, you need metadata that can evolve alongside the data, scale to petabytes of objects, and remain queryable without expensive retrieval.

With S3 annotations, you can store context such as AI-generated transcripts, content ratings, or technical specifications directly alongside your objects. Your context moves automatically with the object during copy, replication, and cross-region transfers, and S3 removes it when you delete the object. When you enable S3 Metadata, annotations automatically flow into fully managed annotation tables that you can query with Amazon Athena and other analytics engines.

Common use cases
Annotations solve complex metadata challenges across industries:

  • Media & Entertainment: Track transcripts, content moderation results, subtitle files, and licensing metadata as separate annotations on video assets, eliminating the need to synchronize metadata across multiple media asset management systems.
  • Financial Services: Attach AI-generated investment summaries and sentiment analysis to research documents, enabling autonomous research agents to discover relevant datasets through natural-language queries without maintaining separate metadata databases.
  • Life Sciences: Annotate clinical trial data with regulatory status, patient cohort details, and approval chains, making compliance audits faster while keeping full context accessible for archived data in Amazon S3 Glacier storage classes without retrieval charges.

How annotations address metadata challenges
Amazon S3 already supports several ways to describe your objects. System-defined metadata captures properties like size and storage class. Object tags support operational tasks like access control and lifecycle management. User-defined metadata lets you add small amounts of custom information at upload time.

While these capabilities work well for their intended purposes, they have limitations when you need to attach much richer context without building and maintaining separate metadata systems. Annotations address these needs by providing metadata capabilities at a fundamentally different scale and flexibility, offering mutable, queryable context per object compared to 10 immutable tags or 2 KB of headers.

Capability Max size Mutable? Best for
System-defined metadata Fixed No Object properties (size, storage class, creation time)
User-defined metadata 2 KB No (set at upload) Small custom key-value pairs
Object tags 10 tags, 128/256 characters per key/value Yes Access control, lifecycle rules, cost allocation
Annotations 1 GB (1,000 × 1 MB) Yes Rich business context (JSON, XML, YAML, plain text)

Today, metadata describing S3 objects often lives in separate databases or sidecar files, requiring complex synchronization workflows that can exceed data storage costs. When you enable S3 Metadata annotation tables, this context becomes queryable at scale through Amazon Athena. AI agents can discover your data through natural language with the S3 Tables MCP server, which provides a standardized interface for AI models to query your annotations. You can query annotations for objects in any storage class, without restoring the objects or paying retrieval charges.

Getting started with annotations
To start using annotations, make sure your AWS Identity and Access Management (IAM) policy or bucket policy grants permissions for the s3:PutObjectAnnotation and s3:GetObjectAnnotation actions. You can then add annotations to any existing or new S3 object using the PutObjectAnnotation API.

For example, a media company can attach technical specifications and AI-produced summaries to a video asset using the AWS Command Line Interface (AWS CLI):

# Create a JSON file with technical metadata
cat > mediainfo.json << 'EOF'
{"codec":"H.265","resolution":"3840x2160","audio_tracks":8,"frame_rate":29.97}
EOF

# Attach it as an annotation
aws s3api put-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name mediainfo \
  --annotation-payload ./mediainfo.json

# Attach a plain-text AI-generated summary as a separate annotation
echo "A 90-minute nature documentary covering wildlife migration patterns across three continents, featuring aerial footage and underwater sequences. Languages: English, Spanish, Portuguese." > ai_summary.txt

aws s3api put-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name ai_summary \
  --annotation-payload ./ai_summary.txt

These commands attach two separate annotations to the same video object. The mediainfo annotation stores structured technical specifications as JSON, while the ai_summary annotation stores a text description. Each annotation is identified by a unique name, and you can read and modify each one independently. With unique names for each annotation, you can use different annotations to support multiple concurrent enrichment workflows, for example, one team adding technical metadata while another team adds content classifications, without interfering with each other.

Retrieve a specific annotation using the GetObjectAnnotation API:

aws s3api get-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name mediainfo \
  ./mediainfo-output.json

To see all annotations attached to an object, use the ListObjectAnnotations API:

aws s3api list-object-annotations \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4

When you no longer need a specific annotation, remove it using the DeleteObjectAnnotation API:

aws s3api delete-object-annotation \
  --bucket my-media-bucket \
  --key videos/documentary-2026.mp4 \
  --annotation-name mediainfo

You can update an existing annotation at any time by calling PutObjectAnnotation again with the same annotation name. For large objects uploaded using multipart upload, attach annotations after completing the multipart upload using the PutObjectAnnotation API.

Querying annotations at scale with S3 Metadata tables
Attaching annotations to individual objects is useful, but the real power comes when you query across all your annotations at scale. When you enable S3 Metadata annotation tables on your bucket, S3 automatically indexes your annotations into a fully managed Apache Iceberg table, called an annotation table. You can query annotation tables with Amazon Athena or any Iceberg-compatible engine.

To enable annotation tables, use the S3 console or the CreateBucketMetadataConfiguration API. The following example creates a new metadata configuration with annotation tables enabled while keeping journal tables for change tracking and disabling the live inventory table:

{
  "JournalTableConfiguration": {
    "RecordExpiration": { "Expiration": "DISABLED" }
  },
  "InventoryTableConfiguration": { "ConfigurationState": "DISABLED" },
  "AnnotationTableConfiguration": {
    "ConfigurationState": "ENABLED",
    "Role": "arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"
  }
}

This configuration tells S3 to automatically capture all your annotations in a queryable table. Once applied, any annotation you attach to objects in this bucket will appear in the table within approximately one hour.

If the bucket already has a metadata configuration, use the UpdateBucketMetadataAnnotationTableConfiguration API:

aws s3api update-bucket-metadata-annotation-table-configuration \
  --bucket my-media-bucket \
  --annotation-table-configuration '{"ConfigurationState":"ENABLED","Role":"arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"}'

Once enabled, your annotations automatically flow into the annotation table. Journal tables update in near real time, while annotation tables refresh within an hour. Unlike traditional metadata tables that require predefined schemas, annotation tables automatically adapt to any JSON, XML, or YAML structure you write. Each annotation becomes a row in the table with its content stored in a text_value column, letting you query across all annotations without schema migrations.

If you enable annotation tables on a bucket that already has annotated objects, S3 automatically backfills existing annotations into the table. The backfill process runs in the background and can take several hours to days depending on the number of objects.

For example, to find all video assets with more than 8 audio tracks across your entire bucket using Amazon Athena:

SELECT DISTINCT bucket, object_key
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."annotation"
WHERE name = 'mediainfo'
AND CAST(json_extract_scalar(text_value, '$.audio_tracks') AS INTEGER) > 8

This query scans the annotation table for all annotations named mediainfo, extracts the audio_tracks field from the JSON content, and returns objects where the count exceeds 8.

Or to find all objects that received new annotations in the last 24 hours through the journal table:

SELECT bucket, key, version_id, record_timestamp, annotation.name
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."journal"
WHERE record_timestamp >= (current_date - interval '1' day)
AND annotation.name IS NOT NULL
AND record_type IN ('CREATE_ANNOTATION', 'DELETE_ANNOTATION')

This query uses the journal table to track annotation changes in near real time, which is ideal for building event-driven workflows that respond to new or deleted annotations.

You can also use natural language to search objects by their annotations using agents in Amazon SageMaker Unified Studio or any IDE with the S3 Tables MCP server. For example, asking “find all PG-rated movies with Spanish subtitles from 2023” returns results in seconds instead of the hours it would take querying multiple disconnected systems.

Get started today
You can start using Amazon S3 annotations today in all AWS Regions, including the AWS China Regions. Annotation tables are available in all AWS Regions where S3 Metadata is available.

Whether you’re building AI agents that need to discover data autonomously, managing petabytes of media assets with complex metadata, or tracking compliance context for archived datasets, annotations give you the scale and flexibility to attach rich metadata directly to your objects without managing separate systems.

Annotation storage is always billed at S3 Standard rates, even if the parent object is in S3 Glacier or another storage class. For full pricing details, visit the Amazon S3 pricing page.

To learn more and get started, visit the Amazon S3 Metadata overview page and the Amazon S3 documentation. Send feedback to AWS re:Post for S3 or through your usual AWS Support contacts.

Daniel Abib

Minisforum S5 All-Flash NAS Shown Based on Intel’s Wildcat Lake Platform

Post Syndicated from Ryan Smith original https://www.servethehome.com/minisforum-s5-all-flash-nas-shown-based-on-intels-wildcat-lake-platform/

At Computex 2026 Minisforum was showing off their upcoming S5 NAS, a mid-range all-flash NAS. With 5 M.2 SSD slots and 10GbE networking, the fanless NAS punches up

The post Minisforum S5 All-Flash NAS Shown Based on Intel’s Wildcat Lake Platform appeared first on ServeTheHome.

Building highly available Oracle databases with Amazon FSx for NetApp ONTAP

Post Syndicated from Vignyanand Penumatcha original https://aws.amazon.com/blogs/architecture/building-highly-available-oracle-databases-with-amazon-fsx-for-netapp-ontap/

Oracle databases power mission-critical enterprise applications, making their continuous availability essential for business operations. Traditional Oracle high availability (HA) solutions require complex clustering software, expensive shared storage arrays, and specialized database administration teams. These conventional approaches often introduce single points of failure while demanding significant operational overhead.

Modern cloud architectures offer a transformative approach that combines Amazon FSx for NetApp ONTAP (FSxN) with Amazon EC2 Auto Scaling groups, automated AMI creation, AWS Lambda-driven orchestration, and AWS Systems Manager Parameter Store (SSM Parameter). This solution removes traditional Oracle HA complexities while delivering enterprise-grade availability, automated recovery, and makes sure new instances launch with the latest Oracle configuration.

This post shows how to build a highly available Oracle database architecture using FSxN shared storage, Auto Scaling groups with dynamic AMI updates, and serverless orchestration to help reduce recovery times with current configurations.

Solution overview

The solution uses multiple AWS services working together to create a comprehensive high availability architecture. FSxN Multi-AZ provides persistent shared storage spanning availability zones for Oracle database files, software, and configurations, so that data remains accessible when EC2 instances are replaced. Auto Scaling groups deliver automated instance lifecycle management with the latest AMI configurations, so failed instances are quickly replaced with identical configurations that can immediately access the existing Oracle database files on FSxN. AWS Backup creates AMIs that capture the latest Oracle host configurations including patches and settings, preserving the complete server state for consistent deployments. AWS Lambda extracts the AMI ID from backup recovery points and updates the SSM Parameter, orchestrating the entire configuration management workflow. Systems Manager Parameter Store stores the current AMI ID for Auto Scaling group launch templates, so new instances always launch with the most recent configuration and can immediately connect to the Oracle database on shared storage.

The following diagram shows the complete architecture with all AWS services and their interactions:

AWS architecture diagram showing Oracle Database disaster recovery across two Availability Zones using FSx for ONTAP synchronous replication, AWS Backup automation with EventBridge and Lambda, and Auto Scaling group with SSM Parameter Store for AMI management.

Key benefits include:

  • Recovery Time Objective (RTO): Can help achieve 2–5 minutes with latest Oracle configuration
  • Recovery Point Objective (RPO): Near-zero through synchronous Multi-AZ replication
  • Configuration consistency: New instances launch with identical Oracle host setup
  • Automated AMI management: Scheduled AMI creation with Parameter Store updates

Walkthrough

This walkthrough demonstrates implementing Oracle HA using Amazon FSx for NetApp ONTAP shared storage, AWS Backup-driven AMI creation, Lambda orchestration, and Auto Scaling groups with Parameter Store integration for configuration consistency and automated failover.

Prerequisites

For this walkthrough, you should have the following prerequisites:

  • An AWS account with appropriate permissions for Amazon FSx, Auto Scaling, EC2, Lambda, and Systems Manager
  • A VPC with subnets in at least two Availability Zones
  • Oracle database software

Keep in mind that customers are responsible for their own Oracle licensing compliance.

  • An EC2 instance with Oracle database installed and configured
  • AWS Identity and Access Management (IAM) roles for AMI creation and cross-service communication
  • Basic knowledge of Oracle database administration and AWS automation

Assumptions

This post is a conceptual illustration of the architecture. Your specific implementation will vary based on your VPC layout, Oracle version, storage requirements, and organizational security policies.

We assume the reader is familiar with:

  • Creating and configuring Amazon FSx for NetApp ONTAP file systems through the AWS console
  • iSCSI concepts including initiators, targets, and multipath I/O
  • Oracle database startup and shutdown procedures
  • AWS Backup, Lambda, and Auto Scaling group fundamentals

For detailed step-by-step instructions on specific AWS services, refer to the additional resources section.

Step 1: Create an Amazon FSx for NetApp ONTAP file system

FSxN Multi-AZ provides the persistent shared storage foundation for this architecture. Unlike Amazon Elastic Block Store (Amazon EBS) volumes, which are bound to a single AZ, FSxN Multi-AZ replicates data synchronously across two AZs with automatic failover. This means that when an EC2 instance is replaced (whether in the same AZ or a different one), the new instance can immediately access the existing Oracle database files without restoring from backup.

To create the file system, navigate to the Amazon FSx console and select Amazon FSx for NetApp ONTAP as the file system type.

The critical configuration choice is selecting Multi-AZ deployment, which places an active file server in one AZ and a standby in another.

Amazon FSx console showing oracle-fsxn-multi-az file system configuration with ONTAP Multi-AZ 1 deployment, 1024 GiB SSD storage, 512 MB/s throughput, spanning us-east-1a preferred and us-east-1b standby subnets.

FSxN console showing Multi-AZ deployment type selection with preferred and standby subnets in separate availability zones.

After the file system is created, you need to set up a Storage Virtual Machine (SVM), which acts as a logical storage container providing data access to your Oracle instances. The SVM creation is done from the FSx console under your file system’s details.With the SVM in place, the next step is configuring iSCSI access. FSxN exposes iSCSI endpoints—these are IP addresses (one per AZ) that your EC2 instances use to connect to the storage over the iSCSI protocol. You can find these endpoint addresses in the FSx console under your SVM’s Endpoints tab.

Amazon FSx Storage Virtual Machine configuration page showing oracle-svm with Created lifecycle state, NFS, iSCSI, and management endpoints for Oracle Database storage connectivity.

SVM Endpoints tab showing iSCSI endpoint IP addresses for each availability zone. These addresses are used in the EC2 instance’s iSCSI discovery configuration.

The iSCSI setup involves creating iGroups (which define which EC2 instances can access the storage) and LUNs (logical storage units mapped to those groups) through the NetApp ONTAP CLI. On the EC2 side, you configure the iSCSI initiator to discover and connect to the FSxN endpoints, then mount the resulting block devices. Using multipath I/O with both endpoints makes sure that Oracle data remains accessible even during an AZ failover. For detailed iSCSI configuration steps, see mounting iSCSI LUNs on Linux clients.

A dedicated security group is required for FSxN access. At minimum, the security group must allow inbound traffic on ports 111 (NFS portmapper), 635 (NFS mountd), 2049 (NFS), 3260 (iSCSI), 4045–4046 (NFS lock), 443 (HTTPS for management), and 22 (SSH for ONTAP CLI). Restrict the source to only your Oracle EC2 instances’ security group.

Step 2: Set up AWS Backup for EC2 instance protection

AWS Backup captures the complete state of your Oracle EC2 instance. The key design choice here is using tag-based resource selection rather than specifying instance IDs directly. Because Auto Scaling groups replace instances (and generate new instance IDs), tag-based selection makes sure that any new instance with the correct tags are automatically included in the backup plan.Configure a backup plan with a frequency appropriate for your environment and set the resource assignment to select EC2 instances matching your application tag (for example, ‘Application: Oracle’).

AWS Backup console showing blog-test backup plan with hourly backup rule targeting Oracle EC2 instances identified by the Application:oracle-db tag.

AWS Backup resource assignment configured with tag-based selection. Any EC2 instances tagged with the application tag are automatically included in the backup plan.

Step 3: Configure Lambda for AMI management

When AWS Backup completes an EC2 backup, it creates an AMI as the recovery point. An Amazon EventBridge rule detects this completion event and triggers a Lambda function. The function extracts the AMI ID from the backup recovery point, updates the SSM Parameter Store parameter with the new AMI ID, and cleans up older AMIs to control storage costs.

AWS Lambda function configuration for oracle-backup-handler showing Python 3.11 runtime, EventBridge trigger, and description indicating it processes AWS Backup completion events and updates AMI in SSM.

Lambda function overview showing the EventBridge trigger, Python 3.11 runtime, and function description indicating its role in processing backup completions and updating AMI references in SSM.

This event-driven approach means the latest AMI is available without manual intervention. The Lambda function needs IAM permissions for EC2 (to manage AMIs), SSM (to update the parameter), and Backup (to read recovery point metadata).

Amazon EventBridge rule oracle-backup-completion configured to trigger the oracle-backup-handler Lambda function when AWS Backup completes an EC2 backup job, with event pattern filtering for COMPLETED state.

EventBridge rule configured to match AWS Backup job completion events for EC2 resources, with the Lambda function as the target.

Step 4: Configure the Systems Manager Parameter Store

The SSM Parameter Store holds the current AMI ID that the Auto Scaling group’s launch template references. The parameter is created with the aws:ec2:image data type, which enables the launch template’s resolve:ssm: functionality, a feature that allows the launch template to dynamically resolve the AMI ID at instance launch time without requiring a template version update.

AWS Systems Manager Parameter Store showing /oracle/ec2/ami-id parameter with AMI value ami-0a705a7d5523c555, version 857, last modified by the oracle-backup-lambda-role on April 25, 2026.

SSM Parameter Store showing the /oracle/ec2/ami-id parameter with aws:ec2:image data type. The “Last modified user” confirms the Lambda function is automatically updating this parameter after each backup cycle.

When Lambda updates this parameter after each backup cycle, the next instance launched by the Auto Scaling group will automatically use the latest AMI. This removes the operational burden of manually updating launch template versions.

Step 5: Set up an Auto Scaling Group with dynamic AMI

The launch template references the SSM parameter using the resolve:ssm: prefix for the AMI ID field. This is the mechanism that ties the entire automation pipeline together. The mechanism backups trigger AMI creation, AMI IDs flow into Parameter Store, and the launch template resolves the latest AMI at launch time.

EC2 Launch Template oracle-db-launch-template version 75 showing AMI ID resolved from SSM parameter resolve:ssm:/oracle/ec2/ami-id with r7i.large instance type for Oracle Database deployment.

Launch template AMI configuration showing the ‘resolve:ssm:’ prefix, which dynamically retrieves the latest AMI ID from Parameter Store at instance launch time.

The Auto Scaling group is configured with minimum, maximum, and desired capacity all set to 1. This is not traditional auto-scaling, it’s a self-healing pattern. The sole purpose is to detect when the Oracle instance becomes unhealthy and automatically launch a replacement. The health check grace period should be set to at least 300 seconds (5 minutes) to allow Oracle sufficient time to start before health checks begin evaluating the new instance.

The launch template also includes a User Data script that runs on each new instance. This script configures the iSCSI initiator, discovers and connects to the FSxN endpoints, mounts the Oracle data volumes, and starts the Oracle database through a systemd service. This automation makes sure that a replacement instance is fully operational without manual intervention.

EC2 Auto Scaling group oracle-db-asg configuration showing desired capacity of 1, scaling limits 1-1, r7i.large instance type, oracle-db-launch-template with Latest version, spanning two availability zone subnets.

Auto Scaling group configured with min=max=desired=1 across two availability zones, providing self-healing capability.

Test the complete workflow

To validate the architecture, simulate an instance failure by terminating the current Oracle EC2 instance.

The expected sequence is:

  1. The Auto Scaling group detects the instance is unhealthy (within approximately 30 seconds)
  2. A new instance launches from the latest AMI resolved from Parameter Store (approximately 2 minutes)
  3. The User Data script connects to FSxN using iSCSI and starts Oracle (approximately 2–3 minutes)
  4. The Oracle database is available and accepting connections (total elapsed: approximately 5 minutes)

Auto Scaling group Activity History showing the self-healing sequence — the unhealthy instance is terminated, and a replacement is launched automatically within seconds.

The new instance automatically inherits the application tags from the Auto Scaling group, which means AWS Backup includes it in the next backup cycle without manual configuration.

Cleaning up

To avoid incurring future charges, delete the resources:

  • Delete Lambda functions and EventBridge rules
  • Remove Parameters from Systems Manager Parameter Store
  • Delete AWS Backup plans and backup vault
  • Deregister created AMIs
  • Terminate Auto Scaling group instances
  • Delete the Amazon FSx for NetApp ONTAP file system

Conclusion

This architecture facilitates Oracle high availability with configuration consistency by combining FSxN persistent shared storage with automated AMI management and AWS Backup protection. The Lambda-driven AMI management from backup recovery points and Parameter Store integration helps make sure that replacement instances launched by Auto Scaling groups always use the latest Oracle host configuration and can immediately connect to the existing Oracle database files stored on FSxN. Replacements occur only when health checks fail. Organizations can target high availability while maintaining configuration consistency across instance replacements. The automated AMI management alleviates configuration drift and makes sure that disaster recovery scenarios restore Oracle instances with identical host-level configurations that can immediately access the persistent Oracle database on shared storage. Healthy instances continue running unchanged, with replacements occurring only, when necessary, because of health check failures.Next steps include implementing cross-Region AMI replication, adding AMI validation testing, and developing custom health checks that verify both Oracle database and host configuration consistency.

Additional resources

Automating contract intelligence with Doczy.ai™ on AWS

Post Syndicated from Sanket Nasre original https://aws.amazon.com/blogs/architecture/automating-contract-intelligence-with-doczy-ai-on-aws/

Extracting actionable insights from thousands of contracts and legal documents remains a challenge. For organizations, critical business information is locked in unstructured documents such as contracts, legal agreements, provider arrangements, and vendor invoices. Extracting and operationalizing this information has traditionally been a manual, error-prone, and resource-intensive process. This leads to missed savings opportunities, costly delays, and significant inefficiencies across the enterprise.

AArete, a global management and technology consulting firm specializing in healthcare, recognized this challenge and developed Doczy.ai™, an intelligent contract interpretation solution powered by generative AI on Amazon Web Services (AWS).

In this post, we show you how Doczy.ai™ uses generative AI on AWS to automate contract intelligence at scale, transforming unstructured documents into structured, actionable insights, so organizations can automate critical business processes and unlock the full value of their data.

The challenge: Data trapped in documents

For healthcare organizations, managing and interpreting contracts and documents represents a major operational bottleneck. Manual review processes require deploying teams to extract data from thousands of documents. This is an approach that is neither scalable nor sustainable, highly prone to error, and costly. Organizations relying on institutional knowledge face additional risks: critical information resides with a few key individuals, creating knowledge silos and succession planning challenges. Existing Contract Lifecycle Management (CLM) systems often prove inadequate for capturing the nuanced and complex terms unique to each agreement. These legacy systems can only configure predefined fields, missing the rich detail and contextual information that distinguishes contracts. The downstream impact is substantial: in healthcare, reimbursement terms must be manually translated into claims systems—a slow, error-prone process. Similarly, verifying vendor invoices against contract terms often requires manual effort, leading to payment processing delays and missed contractual savings opportunities. These inefficiencies ultimately leave significant value on the table.

This is where Doczy.ai™ provides significant value.

Doczy.ai™: An intelligent contract interpretation solution

Doczy.ai™ directly addresses these challenges using advanced AI and scalability on AWS. Developed by AArete, Doczy.ai™ pushes the boundaries of document intelligence. The solution automatically interprets complex documents and converts them into a structured, queryable information repository that allows organizations to unlock the full value of their data and drive smarter decisions.The evolution of Doczy.ai™ reflects rapid AI advancement. Prior to 2020, document processing required manual effort, with individuals processing approximately 100 documents per week. Between 2020–2023, the firm implemented rules-based contract processing, achieving approximately 55% accuracy. The breakthrough came in 2024 with an AI-based processing built on AWS achieved 99% accuracy—a dramatic improvement over the 55% accuracy of traditional rules-based systems.

Doczy.ai™ architecture

Doczy.ai™ is built on a comprehensive AWS architecture designed to handle the entire document processing lifecycle: from the moment a file enters the system to the moment it generates actionable business intelligence.

Doczy.ai is built on a comprehensive AWS architecture designed to handle the entire document processing lifecycle: from the moment a file enters the system to the moment it generates actionable business intelligence.

Architecture of Doczy.ai™

External users access the platform through a secure Next.js frontend, with Amazon Cognito managing authentication and authorization behind the scenes. After authentication, users upload documents directly to Amazon Simple Storage Service (Amazon S3), where durable, scalable object storage ensures nothing is lost and everything is accessible at scale. From there, the real intelligence begins.

An AWS Lambda function triggers Amazon Textract to extract text and metadata from documents in various formats. What sets Doczy.ai™ apart at this stage is its patented “smart chunking” algorithm, a proprietary approach that goes far beyond pulling words off a page. Rather than treating a document as a flat sequence of text, smart chunking preserves hierarchical structure and one-to-many relationships within documents. It uses a combination of semantic and keyword search to decompose text into meaningful, context-aware chunks, applying dynamic parameters to maintain logical relationships throughout. Sequential identifiers and metadata-driven grouping organize these chunks into field groups, detecting overlaps and removing duplications while keeping the document’s natural flow intact.

After chunking, the document enters the dual clustering engine of Doczy.ai™. This two-lens methodology analyzes every contract simultaneously from both a semantic and a structural perspective. On the semantic side, extracted text is converted into embeddings, numerical representations of meaning, and similar ideas are grouped together even when they’re expressed in different words. On the structural side, pattern-recognition algorithms identify clause types, formatting conventions, table layouts, and hierarchical organization, understanding. For example, that a three-nested-level exhibit carries fundamentally different implications than a straightforward attached schedule.These two analyses don’t operate in isolation. Projection algorithms compare the semantic and structural clusters side by side, synthesizing them into a unified, enriched document model that captures both meaning and context. It’s this convergence that drives the 99% accuracy rate of Doczy.ai™. The system doesn’t just read the words, it understands the contract. Advanced large language models (LLMs) then generate structured output grounded in this dual-clustered intelligence.Before output is finalized, the system determines each document’s file class and generates prompts tailored to the extracted text, cluster classification, and domain context. Through few-shot and multi-shot prompting, the platform continuously edits the prompt on domain-specific examples and based on real outputs, creating a feedback loop that compounds accuracy improvements over time.

The resulting structured data flows into Snowflake, forming a centralized repository that powers intelligent dashboards with actionable insights and visualizations. Throughout the entire pipeline, Amazon CloudWatch monitors performance in real time and proactively surfaces issues before they escalate, while AWS Secrets Manager safeguards sensitive information, ensuring that security is not an afterthought, but a foundational layer woven into every stage of the system.

The transformative impact of Doczy.ai™

The results of this AI-powered approach are transformative and measurable. By automating contract interpretation and document processing, Doczy.ai™ has demonstrated significant impact at scale for multiple organizations across healthcare and financial services. The scale of operations over the last 22 months demonstrates the maturity and production readiness of Doczy.ai™. This solution has processed 2.5 million contract documents (50 million pages) with 137 million API calls to Amazon Bedrock and 442 billion tokens—a level of automation and accuracy previously unattainable through manual or traditional document processing approaches. Over this same period, Doczy.ai™ has helped clients achieve approximately 330 million dollars in cumulative direct and indirect savings.The 99% accuracy rate represents significant improvement over the approximately 55% accuracy of rules-based systems and far exceeds manual processing, which is typically affected by fatigue and human error. The 97% reduction in manual processing time translates directly to cost savings and enables organizations to reallocate human resources to higher-value activities that require judgment and strategic thinking.

A use case in action: Business process automation for health plans

For health plans, Doczy.ai™ provides a powerful solution to automate and improve contract management across the entire lifecycle. It ingests existing contracts in both paper and digital formats, integrates with contract management systems such as Coupa and Icertis, and processes new contracts and amendments as they’re executed. It then creates a centralized metadata repository that feeds directly into downstream systems, enabling end-to-end business process automation.This automation unlocks critical capabilities: Organizations can continuously analyze and improve contract terms, identifying opportunities to improve financial performance and operational efficiency. The architecture feeds accurate, up-to-date contract data directly into claims systems, automating the configuration process that previously required manual translation of reimbursement terms and removing manual data entry, configuration errors, and delays. Additionally, the platform helps maintain claim payment accuracy by assessing payments against contract terms, identifying discrepancies, and flagging potential overpayments or underpayments before they occur.By automating manual processes, health plans can adapt quickly to new contract terms and regulatory requirements. The intelligent dashboards and actionable insights provided by Doczy.ai™ enable decision-makers to understand contract performance, identify trends, and take proactive action to optimize financial outcomes.

Getting started with Doczy.ai™

Organizations interested in using Doczy.ai™ to transform document processing and contract management can engage with AArete to discuss their specific use cases and requirements. AArete offers the platform as a Software as a Service (SaaS) solution, enabling rapid deployment without significant infrastructure investment. AArete’s team of experts will configure this solution for your specific document types, domain terminology, and business processes, supporting maximum value from day one.

Conclusion

The challenge of unlocking data from unstructured documents is a major hurdle for many businesses, particularly in healthcare and financial services where contracts and agreements govern critical operational and financial relationships. By embracing intelligent document intelligence on AWS, organizations can solve this long-standing operational challenge and unlock a new frontier of strategic advantage, turning their data into their most valuable asset.

Built on a sophisticated architecture that orchestrates Amazon Cognito, Amazon S3, AWS Lambda, Amazon Textract, Amazon Elastic Container Service (Amazon ECS), Amazon Bedrock, Amazon CloudWatch, and AWS Secrets Manager, Doczy.ai™ demonstrates how modern cloud services can solve complex document-heavy business problems. Its advanced hybrid smart chunking, dual clustering, and prompt optimization techniques form the core of a patented contract intelligence engine.

Doczy.ai™ delivers tangible impact, processing up to 250,000 contract documents per week with 99% accuracy, reducing manual processing time by 97%, and helping clients unlock roughly 330 million dollars in cumulative savings over 22 months. By embracing this intelligent document processing, organizations can turn contracts into a strategic data asset, improving efficiency, accuracy, and profitability while freeing teams to focus on higher-value work.

To learn more about how AArete and Doczy.ai™ can help your organization transform document processing and unlock the value of your unstructured data, visit the AArete website.

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