Batch Sync & FIFO Queue Service

Problem

Systems that sync large datasets often choke on full-table passes: long-running jobs, partial failures, and no guarantee about processing order. This service had to sync big datasets smoothly and process order-sensitive records exactly as they arrived.

Role & approach

I designed the sync pipeline and queue layer for the Laravel backend.

01Source datalarge, externally synced datasets

→

02Chunked jobsslices of rows, safe to retry

→

03FIFO queueorder-sensitive processing

→

04Target storeMySQL, consistent and current

Challenges & solutions

Full-table syncs took too long and failed messily. I moved to chunked batch processing: jobs processed datasets in bounded slices, so a failure only affected one chunk and could be retried cleanly. This accelerated operations by 60–80% and improved data reliability.

Order-sensitive records needed strict ordering. Generic queues can reorder under concurrency. I implemented FIFO queue logic so items were consumed in the exact order enqueued, giving predictable, order-sensitive data workflows.

Outcome

• Sync operations 60–80% faster via chunked batch processing.
• Reliable, retry-safe data synchronization.
• Deterministic FIFO ordering for order-sensitive workflows.