Scalable ETL System for Subscription Data Management and Analytics

The client faces issues with a non-scalable system for collecting and processing large volumes of subscription data from multiple external sources, leading to data corruption, system bugs, and operational downtime. The existing infrastructure struggles to handle increased data loads within required timeframes, resulting in inaccurate analytics and delayed decision-making.

A medium-to-large subscription-based media organization managing a high volume of subscriber data, seeking to optimize data collection, processing, and reporting systems.

• Develop a scalable ETL system capable of handling increasing volumes of subscription and transactional data.
• Implement robust data validation and error detection mechanisms to ensure data integrity.
• Enable real-time or near-real-time notifications for system and data errors to facilitate prompt issue resolution.
• Automate data ingestion, transformation, and loading processes to improve efficiency and reduce manual interventions.
• Ensure system integration compatibility with existing IT infrastructure and analytics tools.
• Achieve improved data accuracy, system reliability, and operational uptime.

• Batch data collection from external data sources such as FTP servers in CSV format at defined intervals.
• Support for data validation algorithms to detect corrupt or inconsistent data structures.
• Parallel processing architecture to enable high-volume data processing within defined time windows.
• Data transformation routines to standardize differing data formats into a unified structure for various use cases.
• Data loading procedures into operational and analytics databases via automation scripts or packages.
• Implementation of monitoring and logging systems for process transparency and health checks.
• Automated alerting mechanisms for critical processing errors or system failures.

• FTP servers for data source ingestion
• Operational databases for transaction processing
• Analytics data repositories for deriving insights

• Scalability to manage increasing data volumes and concurrent processing
• High performance enabling timely data loads within a 24-hour batch window
• System availability and reliability with minimized downtime
• Robust error detection, alerting, and recovery mechanisms
• Secure data handling and compliance with data privacy standards

The implementation of a scalable ETL system is anticipated to double data processing bandwidth, improve data accuracy and integrity, and reduce system failures. This will enable the client to handle growing subscriber data volumes efficiently, provide real-time notifications for data issues, and support enhanced analytics and reporting capabilities, ultimately leading to better subscription management and informed business decision-making.