Business intelligence and operational teams were overwhelmed by the sheer volume of daily transaction records generated by the platform. Traditional CRON jobs running basic SQL scripts were timing out, leading to delayed insights and stale dashboards that executives could not rely on for daily standups.
To resolve this bottleneck, I designed and deployed a fully automated, scalable reporting pipeline built entirely on cloud-native big data technologies. The system was designed to ingest massive daily payloads, aggregate the data, and generate concise, actionable summaries for different business units.
I leveraged PySpark running on AWS Glue to distribute the heavy data processing workloads across ephemeral clusters. By optimizing data partitioning and utilizing columnar storage formats like Parquet, I drastically sped up query times. I also integrated an AI summarization step to extract key trends from the raw data automatically.
The upgraded pipeline successfully processed over 1 million records daily without a single timeout. It transformed a previously fragile, multi-hour manual task into a robust 15-minute automated job, ensuring stakeholders always started their day with fresh data.