Implement a backup technique for Amazon Fast Sight BI belongings

Amazon Fast Sight is a core characteristic inside Amazon Fast — an agentic, AI-powered digital workspace designed to maximise end-user productiveness— that gives AI-powered BI capabilities by means of pure language queries, interactive dashboards, and embedded analytics from trusted enterprise information sources.

Amazon Fast Sight belongings similar to dashboards, analyses, datasets, and information sources could be backed up utilizing the AssetsAsBundle APIs described on this put up. A backup technique helps shield in opposition to unintentional deletions, unintended modifications, and regional disruptions. For groups that depend on Fast Sight to assist essential enterprise selections, a well-designed backup plan is really helpful.

This put up is the primary in a two-part sequence masking backup and restore for Amazon Fast Sight BI belongings:

• Half 1 (this put up): Covers tips on how to design and implement a backup technique, together with asset choice, the APIs accessible for export, and a ready-to-use pattern automation device.
• Half 2: Covers the restore course of. You need to use the backups created in Half 1 to get well belongings after unintentional deletion, unintended adjustments, or as a part of a broader catastrophe restoration plan.

An efficient backup technique is very essential for organizations in closely regulated industries similar to monetary providers, healthcare, and power, for a number of causes:

• Information loss prevention protects in opposition to human errors, unintentional deletions, and occasions like ransomware.
• Assembly restoration goals helps organizations obtain their Restoration Level Goals (RPO) and Restoration Time Goals (RTO), minimizing information loss throughout incidents.
• Audit and reporting helps monitoring and reporting on belongings all through their lifecycle (creation, updates, and deletion).
• Elevated workload resiliency allows fast restoration of programs to earlier states, decreasing downtime and enhancing reliability. This aligns with the Reliability pillar of the AWS Properly-Architected Framework.
• Catastrophe restoration (DR) preparedness supplies a basis for implementing a DR course of that anticipates technology-related disasters and contributes to your group’s enterprise continuity plan (BCP).

For extra details about the catastrophe restoration capabilities of Fast, and tips on how to assess them in opposition to organizational necessities, see the Amazon Quick disaster recovery and resiliency guide.

On this put up, we cowl finest practices for implementing an efficient backup technique for BI belongings in Fast Sight. We begin by masking the choices for choosing the belongings to incorporate in your backup, then clarify the high-level APIs accessible for that goal, and finalize with pattern code that can assist you get began rapidly.

Backup practices for enterprise intelligence

BI programs current distinctive enterprise continuity challenges due to their function in supporting decision-making processes and key stakeholders. You could shield them in opposition to service disruptions by implementing an efficient backup plan. Earlier than constructing this plan, it’s necessary to know the structure and the size to think about as a part of your DR plan.

The previous diagram exhibits that Fast Sight depends on AWS’s international infrastructure throughout a number of AWS Areas to supply excessive availability for Fast Sight belongings, together with information sources, datasets, analyses, and dashboards.

The Tremendous-fast, Parallel, In-memory Calculation Engine (SPICE) shops and encrypts imported information with excessive availability (HA) by means of redundant copies throughout a number of Availability Zones (AZs) throughout the Fast Sight Area.

With this regional design, you possibly can preserve sources in a number of Areas and use a secondary Area within the unlikely occasion of a regional outage affecting your main BI sources.

For person and identification administration, Fast Sight makes use of a single Area that you just outline through the preliminary account subscription course of. The diagram exhibits that this Area hosts person and group identification data and have to be accessible for customers to entry Fast Sight.

For instance, if a person desires to entry a dashboard within the eu-west-1 Area however the Fast Sight primary Area is us-east-1, each Areas have to be accessible to complete the person entry movement. Fast Sight makes use of regional structure with AZs for redundancy. Nevertheless, if what you are promoting wants safety in opposition to the unlikely occasion of a regional outage, you should design your catastrophe restoration (DR) technique accordingly.

Tip: In the event you’re not sure of your Fast Sight primary Area, you possibly can retrieve this data by working the next command:

aws quicksight describe-account-settings --aws-account-id XXXXXXXXXXXX --region us-east-1

Word: This aws quicksight describe-account-settings command specifies us-east-1 because the endpoint Area. In the event you obtain a 200 standing, your identification Area is us-east-1. In any other case, you obtain an error like the next, which instructs you to level to your present identification Area (for instance, eu-west-1):

An error occurred (AccessDeniedException) when calling the DescribeAccountSettings operation: Operation is being referred to as from endpoint us-east-1, however your identification area is us-east-1. Please use the eu-west-1 endpoint.

Defining Fast Sight belongings to incorporate within the backup plan

With a clearer understanding of Fast Sight structure, the following step is choosing the belongings to incorporate in your backup plan, for this you possibly can observe two methods:

Again up particular belongings:

This selection is appropriate whenever you outline a backup or DR technique centered on defending essential belongings for what you are promoting operations which you could conveniently restore after a catastrophe or unintentional deletion. This consists of particular dashboards (and their dependent belongings) that key stakeholders use to make enterprise selections or that working groups (finance, logistics, procurement, and so forth) use to assist continued enterprise operation.

This selection is really helpful whenever you require an easy backup plan and when the BI belongings which might be key to enterprise continuity are a subset of all of the belongings accessible in your Fast Sight occasion.

Again up all belongings:

This technique is really helpful whenever you need to outline a backup technique that covers each versioning and potential catastrophe restoration. By backing up all belongings, you possibly can carry out in-place rollback of any asset to a earlier state if a human error causes an unintended modification or deletion. Moreover, as a result of you’ve got a backup of all belongings in your account, you possibly can choose particular belongings to revive as a part of your DR plan.

This strategy provides you most protection but additionally requires extra advanced orchestration and automation. This put up focuses on this technique and supplies pattern code which you could adapt to attenuate time to manufacturing.

After you choose your technique, select the kind of BI belongings to export. Fast Sight affords the next asset sorts:

• Dashboards: Learn-only belongings focused at reader customers, revealed from an evaluation. You may also save a dashboard to an evaluation to make edits.
• Analyses and dashboards: An evaluation is an editable model of a dashboard. Solely the authors you select can entry it.
• Information sources: An information supply implements the connection to your information, which might come from analytic sources similar to databases or information warehouses, AWS providers similar to Amazon Easy Storage Service (Amazon S3), or third-party software program as a service (SaaS) information suppliers similar to Jira and ServiceNow.
• Datasets: An asset kind that makes use of a knowledge supply to entry exterior information that you should use to organize and construction the information that powers your analyses and dashboards.
• VPC connections: A characteristic that you should use to combine together with your VPC sources similar to databases and information warehouses which might be situated in that VPC or reachable from it (peered VPCs or networks related by means of VPN or AWS Direct Join).
• Themes: A set of styling and look settings which you could apply to a number of analyses and dashboards to match an aesthetic normal that meets your product or company branding wants.

All these belongings have dependencies between one another, with analyses and dashboards on the prime of this dependency chain, as the next diagram illustrates.

Whenever you select the asset sorts to again up, pay attention to these dependencies so you possibly can absolutely restore belongings from the backup. For instance, whenever you again up a dashboard, you additionally must again up its dependencies, which could embody datasets, information sources, VPC connections, and a theme. The following sections clarify how Fast Sight export APIs deal with these dependencies.

Backup course of overview

The mechanism we cowl on this put up makes use of the AssetsAsBundle APIs accessible in Fast Sight. AssetsAsBundle APIs (additionally referenced as AAB APIs) are a set of high-level APIs designed to assist programmatic export and import of Fast Sight sources. They cowl a spread of use circumstances similar to launch administration, backup and restore, cross-account migration, and steady integration and steady supply (CI/CD) workflows.

This set of APIs consists of the next operations:

• StartAssetBundleExportJob: Creates a package deal (bundle) that incorporates the belongings exported as a part of the operation. The package deal is a zipper file with textual content recordsdata. The format could be both JSON or AWS CloudFormation relying on the worth specified within the ExportFormat parameter. Relying on the format, you possibly can import these belongings utilizing the AAB APIs straight or use CloudFormation infrastructure as code (IaC) for provisioning. After the asynchronous operation finishes, the system uploads the bundle to a brief S3 location for downloading.
• StartAssetBundleImportJob: Takes a beforehand exported bundle and restores the belongings packed in it. You need to use the import operation to outline overrides for a large set of parameters similar to asset names and information supply connection parameters (host, port, workgroup, and extra).
• DescribeAssetBundleImportJob and DescribeAssetBundleExportJob: Each AssetBundle operations are asynchronous. You need to use these APIs to explain the operation, ballot for its standing, and act after it finishes. Whenever you carry out an export job, you should use DescribeAssetBundleExportJob to retrieve the DownloadUrl for the bundle, which is legitimate for five minutes. You possibly can renew the URL with additional calls to DescribeAssetBundleExportJob.

Supported belongings and present limitations of AssetsAsBundle APIs

AssetsAsBundle APIs assist an inventory of Fast Sight belongings together with analyses, dashboards, information sources, datasets, shared folders, restricted folders, refresh schedules, themes, and VPC connections. Nevertheless, some asset sorts have limitations.

Unsupported information sources: Adobe Analytics, File, GitHub, Jira, Salesforce, ServiceNow, Amazon S3 (with regionally uploaded manifest recordsdata), and Twitter.

Unsupported datasets: Datasets that comprise machine studying (ML) columns generated utilizing inference by means of related SageMaker ML fashions.

You could exclude these belongings out of your backup plan to keep away from an InvalidParameterValueException error whenever you concern the StartAssetBundleExportJob operation.

To work round this, you possibly can exchange unsupported information sources and datasets by following these procedures.

For Amazon S3 information sources with native manifest recordsdata:

1. Create a brand new Amazon S3 information supply.
2. Add the manifest file to Amazon S3.
3. Reference the manifest file out of your information supply.
4. Substitute the information supply within the dependent datasets utilizing the UpdateDataSet API.

For different unsupported information sources and datasets:

Observe this process to remodel your incompatible dataset right into a suitable one:

1. Create an evaluation related to the information supply you need to assist in your backup.
2. Create a desk visible that shows all dataset columns.
3. Export the information as a CSV file.
4. Create an Amazon S3 dataset utilizing a manifest uploaded to Amazon S3.
5. Replace your analyses and dashboards with the brand new dataset utilizing the exchange dataset performance.

Different belongings to think about as a part of your backup

Though Fast Sight sources are the important thing belongings to again up, you could embody some extra sources and configurations in your backup plan for potential restore or catastrophe restoration conditions.

You possibly can export Fast Sight belongings together with their permissions, together with the customers and teams which have entry to them. You management this by setting the IncludePermissions flag to true.

As a result of every Fast Sight asset is owned by a person, you could again up customers and teams to have a full and restorable backup.

AssetsAsBundle APIs don’t cowl customers and teams, however you should use DescribeUser, DescribeGroup, and DescribeGroupMembership to incorporate this data within the backup.

Along with customers and teams, think about backing up account settings similar to account customization (the DescribeAccountCustomization API), custom-made manufacturers (the DescribeBrand API), and folders (the ListFolders, DescribeFolder, and DescribeFolderPermissions APIs).

Technical implementation

On this part, we cowl tips on how to create an automation that orchestrates the invocation of the Fast Sight APIs wanted to carry out an efficient backup implementation. We offer pattern code on the finish of this part that implements each customers and teams backup and Fast Sight belongings backup.

Backup orchestration movement

The automation device helps three modes of operation: person backup solely, belongings backup solely, and each. This supplies most flexibility whenever you carry out your backup plan. The next diagram exhibits the movement that the device follows relying on the chosen operation mode.

Customers and group backup

The person and teams backup service makes use of the Fast Sight person and group APIs to learn your account’s present state and retailer the retrieved person and group information in Amazon DynamoDB. The service makes use of date-based suffixes for DynamoDB desk names to protect historic backup information and stop overwrites. This enables point-in-time restoration and backup historical past monitoring. This design additionally simplifies restore operations since you don’t must filter by date suffixes whenever you question information inside a particular backup.

Instance for a backup run on 2025-10-19:

• Customers: quicksight-users-backup-2025-10-19
• Teams: quicksight-groups-backup-2025-10-19
• Consumer-Group Memberships: quicksight-users-groups-backup-2025-10-19

Customers Desk Schema:

{
  "user_name": "string (partition key)",
  "arn": "string",
  "e mail": "string",
  "function": "string",
  "identity_type": "string",
  "energetic": "boolean",
  "principal_id": "string",
  "backup_timestamp": "string (ISO 8601)",
  "custom_permissions_name": "string"
}

Teams Desk Schema:

{
  "group_name": "string (partition key)",
  "arn": "string",
  "description": "string",
  "principal_id": "string",
  "members": ["list of user names"],
  "backup_timestamp": "string (ISO 8601)"
}

Customers-Teams Membership Desk Schema:

{
  "membership_id": "string (partition key, format: username#groupname)",
  "user_name": "string",
  "group_name": "string",
  "user_arn": "string",
  "group_arn": "string",
  "backup_timestamp": "string (ISO 8601)"
}

Word: The person and group backup service implements dual-Area assist. Consumer and group operations use the identity_region configuration parameter, whereas backup asset operations use the usual aws_region. This design addresses enterprise situations the place Fast Sight identification administration is configured in a unique Area than asset storage.

Belongings backup

The belongings bundle backup service coordinates the export of belongings inside a Area and uploads the generated bundle to an Amazon S3 location for later use. The automation backs up the next belongings: information sources, datasets, analyses, dashboards, and themes. By default, the backup consists of all dependencies. You possibly can disable this setting if wanted.

At a excessive stage, the service performs the next duties:

• Lists all information sources utilizing the ListDataSources API, filtering out Amazon S3 manifest-based information sources and information sources with invalid VPC connection names. Names should comprise solely alphanumeric characters separated by hyphens.
• Lists all datasets utilizing the ListDataSets API, filtering out FILE datasets by checking the ImportMode subject.
• Lists all analyses utilizing the ListAnalyses API.
• Lists all dashboards utilizing the ListDashboards API.
• Teams belongings by kind for separate export jobs. You possibly can configure the variety of belongings to incorporate in every bundle, with a most of 100 (the API restrict).
• Checks the export job standing utilizing the DescribeAssetBundleExportJob API and implements exponential backoff to keep away from throttling.
• Uploads the finished asset bundle to Amazon S3 utilizing the next prefix construction.

my-QuickSight-backups/
└── QuickSight-backups/                          # Customized S3 prefix
    ├── 2024/01/15/
    │   ├── datasources/
    │   │   ├── datasources-143022.zip            # Single bundle (≤ max_assets_per_bundle)
    │   │   └── datasources_bundle_1-143045.zip   # A number of bundles when belongings exceed restrict
    │   ├── datasets/
    │   │   ├── datasets_bundle_1-143045.zip      # A number of bundles when belongings exceed restrict
    │   │   └── datasets_bundle_2-143045.zip      # Sequential numbering for a number of bundles
    │   ├── analyses/
    │   │   └── analyses-143108.zip               # Single bundle
    │   └── dashboards/
    │       ├── dashboards_bundle_1-143131.zip    # First of a number of dashboard bundles
    │       └── dashboards_bundle_2-143131.zip    # Second dashboard bundle
    └── 2024/01/16/
        ├── datasources/
        │   └── datasources-090015.zip
        ├── datasets/
        │   └── datasets-090030.zip
        └── ...

Word: The bundle quantity string is current solely when the variety of belongings to again up exceeds the configured worth in max_assets_per_bundle.

Finish-to-end device for backup creation

The QuickSight-backup device supplies a easy option to export all of your Fast Sight belongings and their dependencies into sturdy, cheap storage similar to Amazon S3. The device creates new prefixes for generated bundles, so earlier backups aren’t overwritten. The device additionally exports customers and teams utilizing the identical precept: DynamoDB shops this information, and desk names comprise the date when the backup was generated. With this strategy, you should use backups as a supply to your restoration technique and observe the historical past of adjustments to your Fast Sight belongings and related customers.

The code makes use of the Boto3 Python SDK and consists of packaging assist by means of setuptools for setup and use.

Tooling utilization and configuration

Earlier than utilizing the device, ensure you meet the next conditions:

• Python 3.8 or greater.
• A Fast Sight account with Enterprise version or greater.
• AWS Command Line Interface (AWS CLI) configured with acceptable credentials.
• Required AWS permissions. See the Permissions section within the code.

Clone from supply

git clone https://github.com/aws-samples/sample-quicksight-backup-tool.git
cd quicksight-backup-tool

Create a Python venv (really helpful)

python3 -m venv ./.venv
supply .venv/bin/activate

Set up the package deal

Create a configuration file

To get began, discuss with the config-basic.yaml file within the repo or create one from scratch. This configuration file defines key parameters for the device, together with the next:

• AWS account.
• Area.
• Backup areas (DynamoDB tables and Amazon S3 bucket prefixes).

Utilizing the device

After set up, you possibly can run the device as follows:

quicksight-backup --config config.yaml --mode full

You solely want to supply the --config parameter. You possibly can omit the remainder. The --mode parameter controls the backup kind (full, users-only, or assets-only), the place full is the default mode. The next checklist describes the arguments the device helps.

Non-obligatory arguments

• --mode, -m: Backup mode (full, users-only, assets-only); default is full.
• --output-dir, -o: Output listing for stories and manifests.
• --verbose, -v: Allow verbose (DEBUG) logging.
• --log-file: Path to log file.
• --dry-run: Validate configuration with out working the backup.
• --no-progress: Disable progress indicators.
• --generate-manifest: Generate a backup manifest file.
• --generate-report: Generate a human-readable backup report.
• --version: Present model data.

For extra data, see the device README file.

Device code

You will discover the code for this device within the aws-samples repository. This device helps you get began rapidly. Use it as a foundational reference to refine and adapt to your particular backup necessities.

Earlier than you implement a backup answer in your manufacturing setting, affirm that you just:

• Evaluate and adapt the code to align together with your particular infrastructure necessities, safety insurance policies, and compliance requirements.
• Conduct thorough testing in a non-production setting to validate performance and efficiency.
• Implement acceptable safety controls together with encryption, entry administration, and audit logging required by your group.
• Validate restoration procedures to substantiate your backup technique meets your outlined Restoration Time Goals (RTO) and Restoration Level Goals (RPO).
• Think about value optimization methods and monitoring to maintain the answer inside your operational finances.
• Keep away from concurrent device execution: This device depends on the AssetsAsBundle APIs, which have low throttling thresholds. The pattern device will not be designed to run a number of situations in parallel throughout the identical AWS account. If a number of groups want to make use of the device, think about implementing a concurrency management mechanism (for instance, a lock desk in DynamoDB or a database-level lock) to stop concurrent runs that might set off API throttling.

Scheduled execution

The pattern device described within the earlier part is designed for on-demand execution and is properly suited to getting began or working ad-hoc backups. For a production-grade backup technique, you may need to automate backup runs on a recurring schedule in order that your Fast Sight belongings are constantly protected with out guide intervention.

This part outlines the high-level structure for a scheduled, absolutely automated backup answer. Detailed implementation and code for this structure are outdoors the scope of this put up.

Structure overview

The scheduled execution structure is constructed on three AWS managed providers that work collectively to supply a dependable, serverless, and cost-effective automation pipeline:

• Amazon EventBridge is the scheduler. It triggers the backup workflow at an outlined cadence, for instance, every day at midnight. EventBridge guidelines allow you to outline versatile cron-based or rate-based schedules with out managing any underlying infrastructure.
• AWS Step Features is the orchestration layer. It coordinates the run of the person backup steps within the appropriate sequence. Step Features supplies built-in error dealing with, retry logic, and execution historical past, which makes it properly suited to long-running workflows that span a number of API calls and asynchronous operations.
• AWS Lambda implements every particular person backup step as an unbiased, stateless perform. Splitting the backup logic throughout a number of Lambda capabilities addresses the time constraints inherent within the backup course of. Every export job is asynchronous and may take a number of minutes to complete, relying on the quantity and measurement of belongings being exported.

Workflow steps

As a result of the end-to-end backup course of can take a major period of time, the automation is decomposed into discrete steps, every carried out by a devoted Lambda perform. AWS Step Features orchestrates these capabilities in sequence, passing state between them and dealing with retries for transient failures. The workflow consists of the next steps:

1. Customers and teams backup: Retrieves all Fast Sight customers, teams, and group memberships utilizing the Fast Sight identification APIs and persists the information to DynamoDB with date-based desk suffixes, as described within the Technical implementation part. This operation can run in parallel with the asset backup operations as a result of it doesn’t have any dependency.
2. Asset backup discovery: Lists all Fast Sight belongings within the goal Area (information sources, datasets, analyses, and dashboards), applies the mandatory filters to exclude unsupported asset sorts, and teams belongings into lists of as much as 100 gadgets every. The output of this step is handed to subsequent steps as enter.
3. Generate bundle: Initiates export jobs for all of the belongings included within the checklist specified because the enter parameter, polls for job completion, and uploads the ensuing ZIP bundles to the designated Amazon S3 prefix.
4. Examine standing: Periodically polls the energetic bundle execution and notifies the AWS Step Features state machine when the export finishes.

The next diagram illustrates the high-level movement of the scheduled execution structure.

Key design concerns

• Asynchronous polling: The check-status Lambda perform polls the job initiated by the generate-bundle Lambda perform utilizing the DescribeAssetBundleExportJob API till the job reaches a terminal state (SUCCESSFUL or FAILED). The check-status Lambda perform runs in a loop with a ready situation (for instance, 30 seconds) between calls.
• Parallelism: Configure an ample stage of parallelism to manage the amount of API calls carried out by the steps in your workflow, particularly on the generate-bundle step that calls the DescribeAssetBundleExportJob and StartAssetBundleExportJob APIs, which have low concurrent fee limits. You need to use the inline map state MaxConcurrency subject to restrict the variety of concurrent runs of the generate-bundle step.
• Error dealing with: Step Features permits you to outline catch blocks and retry insurance policies at every stage. A failure in a single step (for instance, an unsupported asset kind) doesn’t abort your complete backup run.
• Price: When scheduling is enabled, prices scale with backup frequency and retention interval. For steering on estimating storage prices, see the Price estimation part.

Price estimation

The next sections estimate the prices of working the backup device on Amazon S3 (for asset bundles) and DynamoDB (for person and group metadata).

Amazon S3: asset bundle storage

Asset bundles are compressed ZIP recordsdata uploaded to Amazon S3 after every export job. Primarily based on the answer design, every bundle of as much as 100 belongings averages roughly 500 KB when compressed.

Key takeaway: Amazon S3 storage prices for asset bundles are minimal. Even for very massive Fast Sight deployments with 1000’s of belongings, the compressed bundle measurement stays within the low megabytes vary, leading to a month-to-month storage value properly beneath $0.01.

Amazon DynamoDB: person and group metadata storage

Consumer and group data is saved in DynamoDB tables with date-based suffixes to protect backup historical past. DynamoDB storage is priced at roughly $0.25 per GB per thirty days (Customary desk class, on-demand mode).

Every merchandise saved in DynamoDB represents a single person or group definition (together with all related attributes similar to ARN, e mail, function, group memberships, and backup timestamp). Primarily based on the schema described on this put up, the common merchandise measurement is roughly 256 KB.

You need to use this formulation to estimate the dimensions of your DynamoDB tables:

Desk measurement estimate = Variety of gadgets × Common merchandise measurement (256 KB)

Key takeaway: For small and medium organizations, DynamoDB storage prices stay minimal (below $0.10 per thirty days per backup snapshot). For big organizations with tens of 1000’s of customers, prices are nonetheless modest, within the low single-digit greenback vary per snapshot.

Abstract

For a single, unscheduled backup run, the full AWS value is successfully close to zero, dominated by just a few cents of Amazon S3 and DynamoDB storage at most. In the event you implement scheduled backups (coated within the Scheduled execution part), prices scale linearly with backup frequency and retention interval. Even with every day backups retained for 90 days, whole storage prices stay within the low single-digit greenback vary for many deployments. Think about using Amazon S3 Lifecycle insurance policies and DynamoDB Customary-IA to optimize prices as your backup historical past grows.

Conclusion

On this put up, we coated tips on how to design and implement a complete backup technique for Amazon Fast Sight belongings so you possibly can preserve enterprise continuity, meet regulatory necessities, and shield in opposition to information loss.

We coated tips on how to use AssetsAsBundle APIs to programmatically export and protect essential BI belongings, together with dashboards, analyses, datasets, and information sources, together with their dependencies and permissions. That can assist you get began, this put up features a pattern automation device which you could check and adapt to your group’s wants. The code orchestrates these APIs, shops asset bundles in Amazon S3, and preserves person and group data in DynamoDB for point-in-time restoration.

Prepared to guard your Fast Sight BI belongings? Get began immediately by cloning the pattern backup device from the AWS Samples repository and testing it in your non-production setting. Start with a easy configuration to again up your most crucial dashboards, then develop to a production-ready backup technique as you validate the method. To be taught extra about Amazon Fast Sight, see the Amazon Fast Sight Consumer Information.

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