Lovepop

The challenge

Lovepop's order and event data flowed from Amazon SQS into NetSuite as the central operations and financial platform. As message volume scaled, the SQS queue began receiving malformed and invalid JSON records — caused by upstream data inconsistencies — that caused the integration pipeline to fail silently or block subsequent valid records from processing. Without graceful error handling, a single bad record could halt pipeline progress and delay downstream NetSuite processing for valid orders.

What we built

• Enhanced the Amazon SQS-to-NetSuite integration with a malformed JSON detection layer that identified invalid records before they entered the processing pipeline
• Implemented dead-letter queue (DLQ) routing so invalid and unparseable messages were automatically moved to an isolated error queue for review without blocking valid record processing
• Built retry logic with configurable backoff for transient parsing failures, distinguishing between recoverable errors and permanently malformed records
• Created error alerting and notification workflows so the Lovepop engineering team received immediate notification of DLQ message accumulation or parsing failure spikes
• Established error record logging with full message payloads and failure reasons to support root cause analysis and upstream data quality remediation

The outcome

• SQS pipeline blockage caused by malformed JSON records eliminated, with invalid messages routed to DLQ without disrupting valid order processing
• NetSuite processing continuity improved as the pipeline no longer halted on individual record failures during high-volume periods
• Mean time to detect and respond to data quality issues reduced through automated alerting on DLQ accumulation
• Engineering team gained full observability into pipeline health and error patterns, enabling proactive upstream data quality improvements