Chunk the source data
Large files were read in manageable pieces instead of loading the complete dataset into memory.
How a memory-heavy data process became a reliable background pipeline that could handle more than one million records.
Processing a large dataset inside one request created memory pressure, timeouts, and poor visibility. If one step failed, the entire workflow often had to be restarted.
Large files were read in manageable pieces instead of loading the complete dataset into memory.
Each chunk became a Laravel job for validation, transformation, and persistence, allowing workers to process the workload in parallel.
Retries, failed-job handling, batch callbacks, and progress updates made long-running processing visible and recoverable.
Exports and final reports were produced asynchronously and stored in S3 without keeping a browser request open.
Large data workflows become easier to scale and support when they are split into small, observable, retryable jobs instead of one long-running process.