@PradeepG0495

⚠ This is a GenAI-powered tool. All generated answers require validation against the provided references.

Creating an End-to-End Solution for Table Data Processing

To build a robust solution for loading table contents, processing records, and creating cases, you can implement the following architecture:

1. Data Loading and Integration

• Utilize Pega's data management tools to establish connections with your data sources
• Configure Database Table Data Sets to connect to your source tables
• For batch processing, set up batch import data jobs to handle the initial data load
• For real-time processing, configure real-time data jobs with streaming data sets

2. Record Processing and Tracking

• Implement a batch queue table to track the status of each record throughout the processing lifecycle
• Use status values to monitor record progression:
  • "New" - Initial state when records are loaded
  • "In progress" - When the queue processor selects a record for processing
  • "Completed" - When processing completes successfully
  • "Process Error" - When a record fails during processing

3. Case Creation

• Define appropriate case types based on your business requirements
• Create data transforms to map the source data to case properties
• Implement case creation logic in your data flow or queue processor

Ensuring All Records Are Accounted For

To maintain complete record accountability throughout the process:

1. Implement Status Tracking:
   • Maintain a comprehensive status tracking mechanism in your batch queue table
   • Each record should have a clear status indicator at all times
   • Include timestamps for each status change to enable auditing
2. Record Counting and Reconciliation:
   • Implement record counting at each stage of the process
   • Compare input record count with processed record count
   • Generate reconciliation reports to identify any discrepancies
3. Monitoring and Alerting:
   • Use the Data Jobs feature to view the number of processed records
   • Configure alerts for unexpected processing patterns
   • Set up monitoring for long-running processes that might indicate issues

Re-processing Erroneous Records and Tracking

For effective error handling and reprocessing:

1. Error Identification and Classification:
   • Mark failed records with a "Process Error" status in the batch queue table
   • Categorize errors by type to facilitate targeted resolution
   • Store error details for troubleshooting purposes
2. Reprocessing Mechanism:
   • Implement a dedicated reprocessing flow for failed records
   • Allow for selective reprocessing based on error type or batch
   • Maintain the original record ID to ensure traceability between attempts
3. Error Tracking and Reporting:
   • Track the number of processing attempts for each record
   • Implement a threshold for maximum retry attempts (e.g., after 3 failures, mark as "Error - Retry limit exceeded")
   • Generate reports on error patterns to identify systemic issues
4. Data Flow Error Handling:
   • Configure appropriate error thresholds in your Data Flows
   • Utilize the Data Flow progress page to monitor failed records
   • Implement error handling flows to manage exceptions not caught in Data Pages or Data Transforms

Getting Delta Records Since Last Run

To efficiently process only new or changed records since the last run:

1. For Database Table Data Sets:
   • Add a timestamp column in your source table to track record changes
   • Configure your Data Set to filter records based on this timestamp
   • Store the timestamp of the last successful run for comparison
2. Using Real-time Data Jobs:
   • Configure real-time data jobs with delta update mode
   • This mode adds new records and updates existing records that have changed
   • Ensures only modified data is processed in subsequent runs
3. Implementing Change Data Capture:
   • If your database supports it, utilize Change Data Capture (CDC) features
   • Configure your data flow to consume only the change log entries
   • This provides an efficient way to identify and process only modified records
4. Tracking Processed Records:
   • Maintain a separate tracking table with record IDs and processing timestamps
   • Use this table to filter out already processed records in subsequent runs
   • Implement a join or exists condition in your data source query

By implementing these approaches, you'll create a comprehensive solution that ensures all records are properly accounted for, errors are effectively managed and reprocessed, and only new or changed records are processed in subsequent runs.

Pega Platform Documentation - Batch import data jobs Pega Platform Documentation - Configuring import data jobs Pega Platform Documentation - Error handling Pega Platform Documentation - Troubleshooting Data Flows Pega Platform Documentation - Data management and integration