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Scalable ETL Module Development for Subscription Data Management

lightpointglobal.com

Business services

Media

Information technology

Legacy System Limitations

Existing non-scalable subscription data collection and processing system causing frequent data corruption, system bugs, and malfunctions. Requires modern ETL solution to handle growing data volumes from multiple external sources while maintaining data integrity and system reliability.

About the Client

Subscription management technology provider offering software solutions for subscription-based businesses, serving top 10 US News Media companies and managing 1M+ subscribers annually

Core Project Goals

Develop scalable ETL module for secure data ingestion from diverse external sources
Implement batch processing with 24-hour interval scheduling
Establish parallel processing capabilities to handle increasing data volumes
Ensure real-time system monitoring and error notification
Create standardized data structures for analytics and operational databases

System Functionality Overview

Automated FTP data ingestion from multiple customer sources
Data standardization and transformation pipelines
Parallel processing for scalability
Data validation algorithms with alert system
Integrated logging and monitoring dashboard
Scheduled SSIS package execution for database synchronization

Technology Stack

.NET

TSQL

SSIS

MSSQL

System Integrations

FTP servers for source data retrieval
Customer external data sources
Target subscription management system
Analytics database infrastructure

Operational Requirements

Horizontal scalability for growing data volumes
24-hour batch processing SLA
Real-time error detection and alerting
Data integrity validation mechanisms
High-availability system architecture

Expected Business Outcomes

Secure data pipeline between customer sources and target system, doubled data processing capacity, scalable architecture for future growth, real-time system health monitoring, reduced downtime through proactive error detection, and standardized data structures ensuring accurate analytics and reporting.