Automated Archiving for Data Model Views

Several Data model views contain large volumes of historical data, which can impact performance, scalability, and overall user experience. A potential improvement would be to implement a historical data archiving strategy by separating older records from active datasets.

For example, records beyond a defined retention period could be moved to dedicated archive views, while primary views retain only the most recent and frequently accessed data. Another option would be to maintain a separate historical reporting layer specifically designed for long-term analysis and archival reporting.

This approach would help improve query performance, reduce dataset sizes, and enhance usability for analytics and reporting tools. Users would benefit from faster data retrieval, shorter dashboard load times, and a more responsive self-service analytics experience, while also reducing processing overhead on the platform.