Currently, the Business Central data retention policy framework processes tables sequentially (ordered from lower to higher table IDs) and enforces a global limit of 10,000 deletions per job execution across the entire environment.

In scenarios involving multiple high-volume tables—such as large-scale e-commerce integrations like Shopify—lower ID tables (e.g., Shpfy Data Capture) completely consume the deletion quota during each execution cycle. Consequently, subsequent critical tables (e.g., Shpfy Order Header / Table 58033) never reach their execution step, leading to an uncontrolled accumulation of obsolete records and operational backlogs.

To resolve this limitation and ensure scalable, automated data maintenance, we request the following improvements to the retention policy framework:

1. Parallel Processing: Allow retention policies to execute concurrently or independently across different tables rather than strictly sequentially.

2. Per-Table Quotas: Replace or supplement the global deletion cap with configurable, table-specific deletion limits so that high-volume logs do not block critical document or header table cleanups.

3. Advanced Scheduling/Prioritization: Provide options to define execution priorities or dedicated execution windows for specific tables within the framework.