Dashboard Integration with External Data Source - Managing Static and Dynamic Updates

Question & Answer
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1

Hi all,

We’re developing a risk assessment dashboard that combines quarterly data with real-time updates from an external provider. Here’s our specific challenge:

Current Setup:
Base data: Static, updated quarterly (risk scores, country metrics)
Dynamic elements: Daily updates (policy changes, news, alerts)
Need to monitor both types of changes

Requirements:

Primary: Track changes between quarterly updates
Example: Major policy change that could impact our quarterly risk scores
Need to flag these changes before next quarterly refresh
Enable proactive response rather than waiting for next update

Secondary: Automated detection of:

Updates to static/quarterly data
Real-time alerts from provider’s platform
Changes that could affect our risk assessment

Question: What’s the best way to:

Link our dashboard to detect these changes?
Set up notifications/alerts?
Trigger actions based on significant updates?

Appreciate any suggestions on approach or similar implementations!

Thank you!

2

AWS Risk Assessment Dashboard Integration Strategy

Summary

For your risk assessment dashboard that combines quarterly data with real-time updates, I recommend implementing a multi-layered approach using AWS EventBridge for event detection, Amazon S3 for data storage with versioning, AWS Lambda for processing changes, and Amazon SNS for notifications. This architecture enables automated detection of both quarterly data updates and real-time changes while providing customizable alerting mechanisms.

Architecture Recommendation

Data Storage and Change Detection

For Quarterly Data:

  • Store quarterly data in Amazon S3 with versioning enabled
  • Implement S3 event notifications to trigger Lambda functions when new quarterly data is uploaded
  • Use AWS Glue for ETL processing of quarterly data into your analytics format

For Real-time Updates:

  • Use AWS EventBridge to capture events from your external provider’s API
  • Create custom event patterns to filter for relevant policy changes, news, and alerts
  • Store real-time updates in Amazon DynamoDB with time-based attributes for efficient querying

Integration Approach

  1. Unified Data Layer:

    • Create a data lake in Amazon S3 that stores both quarterly snapshots and processed real-time data
    • Use AWS Glue Data Catalog to maintain metadata about both data types
    • Implement AWS Lake Formation for fine-grained access control

  2. Change Detection System:

    • Deploy Lambda functions that compare incoming real-time data against quarterly baselines
    • Use DynamoDB streams to trigger analysis when new real-time data arrives
    • Implement configurable thresholds for what constitutes a “significant” change

  3. Notification Framework:

    • Set up Amazon SNS topics for different alert categories (policy changes, risk score impacts, etc.)
    • Configure Amazon CloudWatch alarms for threshold-based alerting
    • Use AWS Chatbot for integration with Slack/Microsoft Teams for operational visibility

Dashboard Integration

For the dashboard itself, I recommend:

  • Amazon QuickSight for visualization with direct integration to your data sources
  • Embedded alerts within the dashboard using QuickSight custom actions
  • Anomaly detection using Amazon QuickSight ML Insights to automatically identify unusual patterns

Implementation Steps

  1. Set up event-driven architecture:

     Example Lambda function to process real-time updates
def lambda_handler(event, context):
    # Extract data from event
    update_data = extract_update_data(event)
    
    # Compare against quarterly baseline
    baseline = get_quarterly_baseline(update_data['category'])
    impact_score = calculate_impact(update_data, baseline)
    
    # If significant impact detected
    if impact_score > THRESHOLD:
        # Store the detection
        store_detection(update_data, impact_score)
        
        # Trigger notification
        send_notification(update_data, impact_score)

  2. Configure EventBridge rules:

    • Create rules that match patterns in your external provider’s events
    • Route matching events to appropriate Lambda functions or SQS queues

  3. Implement notification system:

    • Set up SNS topics for different alert types
  • Configure subscription filters based on alert severity and category
  1. **Dashboard integration:
    • Use QuickSight’s API to programmatically refresh datasets when significant changes occur
    • Implement custom actions in QuickSight to allow users to drill down into alerts

Monitoring and Maintenance

  • Set up CloudWatch dashboards to monitor the entire system
  • Implement dead-letter queues for failed event processing
  • Create automated testing for your change detection algorithms
  • Schedule regular reviews of alert thresholds and detection logic

Cost Optimization

  • Use S3 Intelligent-Tiering for quarterly data storage
  • Implement Lambda provisioned concurrency only during high-traffic periods
  • Consider DynamoDB on-demand capacity for fluctuating workloads

This architecture provides a scalable, event-driven approach to monitoring both your quarterly data and real-time updates, with automated detection and notification capabilities that will enable proactive risk management.

3

Hi @batoula

It’s been a while since we last heard from you. If you have any further questions, please let us know how we can assist you.

If we don’t hear back within the next 3 business days, we’ll proceed with close/archive this topic.

Thank you!

4

Hi @batoula

Since we have not heard back from you, I’ll go ahead and close/archive this topic. However, if you have any additional questions, feel free to create a new topic in the community and link this discussion for relevant information.

Thank you!