End of Day Processing

End of Day Processing

Introduction

Maintainer: adam_nieves@bmc.com

In the highly regulated banking and financial services insurance industry, ensuring timely and accurate processing of daily transactions is critical. Batch End-of-Day (EOD) processing orchestrated by Control-M provides an automated, reliable, and efficient solution for handling complex data workflows. This involves collecting, validating, transforming, reconciling, and archiving data from multiple sources, ensuring that all transactions are processed in accordance with regulatory and business requirements.

Control-M ensures seamless orchestration by automating key tasks such as file transfers, data validation, report generation, and backup. It provides real-time monitoring, error handling, and proactive alerts, giving financial institutions the visibility they need to meet service level agreements (SLAs) and minimize operational risks.

By leveraging Control-M, financial organizations can streamline their EOD processing, improve data accuracy, and ensure compliance with regulatory standards, all while optimizing resource utilization and reducing manual intervention. This allows banks and insurance companies to meet customer expectations and regulatory deadlines with confidence.

Use Case Overview

This Control-M workflow, titled “zzz-eod-process,” is designed to ensure efficient and accurate end-of-day (EOD) processing. It automates the critical steps of data collection, validation, transformation, reconciliation, reporting, backup, and SLA management. By orchestrating these processes, Control-M guarantees data integrity, system reliability, and compliance with service level agreements (SLAs).

Use Case Technical Explanation

Below is a breakdown of the workflow’s components:

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1. Data Collection

   • Purpose: The workflow begins with gathering data from various sources such as transaction logs, customer records, and external data feeds.
   • Control-M Jobs:
     • File Transfer: Transfers data files between systems (e.g., zzz-data-collection job).
     • Database Query: Executes embedded queries to pull relevant data from databases (e.g., zzz-db-record-collection job).
     • File Monitoring: Watches specified directories for file creation, ensuring timely data intake (e.g., zzz-file-creation-watcher job).
   • Example: Transfer transaction data from distributed file systems to a central location for validation.

2. Data Validation

   • Purpose: Ensures the accuracy and integrity of collected data before further processing.
   • Control-M Jobs:
     • Data Assurance: Validates data completeness, consistency, and correctness using specified templates (e.g., zzz-data-quality-check job).
     • Database Standards Check: Uses embedded queries to ensure data meets required standards (e.g., zzz-db-query-standards-check job).
     • Talend Data Management: Executes validation tasks via Talend to ensure reliable data handling (e.g., zzz-talend-data-validation job).
   • Example: Verifying that all transactions have correct account numbers and no missing data points.

3. Data Transformation

   • Purpose: Transforms the validated data into the required formats for downstream processes.
   • Control-M Jobs:
     • Apache NiFi: Automates data flow between systems (e.g., zzz-apache-nifi-data-flow job).
     • AWS Glue: Performs ETL (Extract, Transform, Load) tasks (e.g., zzz-aws-glue-data-transformation job).
     • Informatica: Executes data integration and governance tasks using Informatica PowerCenter (e.g., zzz-information-data-ETL job).
   • Example: Converting raw transaction data into a standardized format suitable for reconciliation.

4. Reconciliation

   • Purpose: Matches and verifies data against internal and external records to ensure all transactions are accurate.
   • Control-M Jobs:
     • Azure Data Factory: Orchestrates and automates data movement for reconciliation purposes (e.g., zzz-adf-data-reconciliation job).
     • Hadoop MapReduce: Processes large datasets in a distributed environment for comprehensive reconciliation (e.g., zzz-hadoop-map-reduce job).
   • Example: Comparing transaction data with bank statements to ensure all entries are correct and accounted for.

5. Reporting

   • Purpose: Generates reports for internal stakeholders, regulatory compliance, and customer communication.
   • Control-M Jobs:
     • Microsoft Power BI: Performs dataset refreshes for up-to-date reporting (e.g., zzz-power-bi-refresh job).
     • Tableau: Refreshes data sources to provide the latest insights (e.g., zzz-tableau-refresh job).
   • Example: Creating daily reports to show transaction summaries and data reconciliation results.

6. Data Backup

   • Purpose: Backs up and archives data to ensure integrity and long-term retention.
   • Control-M Jobs:
     • NetBackup: Performs backups according to specified policies (e.g., zzz-netbackup-policy job).
     • AWS S3 Glacier: Archives data to secure long-term storage using AWS S3 Glacier (e.g., zzz-s3-glacier-data-archiving job).
   • Example: Storing all processed transaction data securely on AWS Glacier for long-term retention and regulatory compliance.

7. SLA Management

   • Purpose: Ensures that all workflow components are completed within agreed-upon timeframes.
   • Control-M Job:
     • SLA Management: Monitors SLA adherence and ensures timely completion of all tasks (e.g., zzz-sla-management job).
   • Example: SLA management tracks the entire workflow, ensuring completion by 12:00 daily, and triggers notifications if any step is at risk of breaching the SLA.

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Conclusion

This EOD processing workflow orchestrated by Control-M ensures that all critical processes—from data collection to reconciliation and reporting—are executed efficiently and accurately. Control-M’s comprehensive automation and monitoring capabilities provide error handling, proactive notifications, and SLA management, ensuring timely completion and data integrity across the entire workflow.

To view the demo flow code-base, and all artifacts, please navigate to the End of Day Processing Git Repository

Job Types Included

• Control-M Managed File Transfer
• Control-M Databases
• Control-M Data Assurance
• Control-M for Talend Data Management
• Control-M for Apache Nifi
• Control-M for AWS Glue
• Control-M for Informatica
• Control-M for Azure Data Factory
• Control-M Hadoop
• Control-M NetBackup
• Control-M for PowerBI
• Control-M for Tableau
• Control-M SLA Management

Demo Environment Information

Environment	Status	Folder
Helix Production	Coming soon!
Helix Pre-Production	Coming soon!
VSE CTM PROD	Available	zzz-end-of-day-processing
VSE CTM QA	Coming soon!