Overview

To comply with client data retention and cleanup requirements, an automated solution was designed and implemented to securely archive, permanently remove, and restore customer data across multiple database tables within a Microsoft SQL Server environment.

The solution enables controlled data purging based on customer identifiers provided by the client and ensures that the deleted data can be fully restored whenever required.

Business Requirement

The client periodically requests the removal of customer-related information from the production database. These requests are provided in the form of CSV files containing one or more Customer IDs.

The primary objectives were:

• Archive all customer-related data before deletion.
• Perform hard deletion of customer data across multiple dependent tables.
• Maintain the ability to restore the deleted data on demand.
• Eliminate manual intervention and reduce operational risk through automation.
• Ensure data consistency and auditability throughout the process.

Technical Challenges

The customer information was distributed across multiple relational tables with complex dependencies. Manual data cleanup presented several challenges:

• Risk of accidental data loss.
• Possibility of orphaned records and referential integrity issues.
• Time-consuming manual execution.
• Difficulty in restoring deleted data when requested by the client.
• Need for a repeatable and auditable process.

Solution Architecture

An automated archival and cleanup framework was developed using SQL Server Integration Services (SSIS) and Microsoft SQL Server.

Step 1: Backup Table Creation

A one-time database script was developed to create backup tables corresponding to each production table involved in the cleanup process.

Key Features:

• Backup tables maintain the same schema structure as source tables.
• Designed to store complete customer-related records before deletion.
• Provides a secure recovery mechanism for future restoration requests.

Step 2: Customer ID File Processing

The process accepts a CSV file containing one or more Customer IDs supplied by the client.

Key Features:

• Automated ingestion of Customer IDs into a staging/session table.
• Validation of input data before processing.
• Supports bulk customer cleanup requests.

Step 3: Data Archival

Before any deletion occurs, all records associated with the specified Customer IDs are copied from the production tables to the corresponding backup tables.

Key Features:

• Preserves complete customer data.
• Maintains relationships between dependent tables.
• Ensures recovery readiness.

Step 4: Data Purge (Hard Delete)

After successful archival, customer data is permanently removed from the production environment.

Key Features:

• Automated deletion across multiple related tables.
• Dependency-aware execution sequence.
• Transaction-controlled processing to ensure data integrity.

Step 5: Data Restoration (Rollback)

Whenever the client requests recovery of previously deleted customer data, the archived records are restored from the backup tables back into the production tables.

Key Features:

• Complete data restoration capability.
• Preserves original relationships and dependencies.
• Minimal downtime and manual effort.

Automation Framework

The entire workflow was automated using SQL Server Integration Services (SSIS).

SSIS Responsibilities

• CSV file ingestion.
• Customer ID validation.
• Data archival processing.
• Data purge execution.
• Logging and error handling.
• Restoration workflow support.
• Audit trail generation.

Technology Stack

Database Platform - Microsoft SQL Server

ETL & Automation - SQL Server Integration Services (SSIS)

Input Source - CSV Files

Backup Repository - SQL Server Backup Tables

Recovery Mechanism - Automated Restore Scripts / SSIS Workflow

Benefits Achieved

Operational Efficiency

• Eliminated manual data cleanup activities.
• Reduced processing time significantly.

Data Protection

• Ensured no customer data is permanently lost.
• Enabled rapid restoration when required.

Auditability

• Complete tracking of archived and deleted records.
• Improved compliance and governance.

Scalability

• Supports single or bulk customer cleanup requests.
• Easily extendable to additional tables and business domains.

Reliability

• Reduced human error.
• Improved consistency and repeatability of operations.

Outcome

The implemented solution provided a fully automated customer data archival, purge, and recovery framework within the Microsoft SQL Server ecosystem. By leveraging SSIS and structured backup repositories, the organization achieved secure data management, faster execution, improved compliance, and a reliable rollback mechanism for future business requirements.