The workflow starts by loading transactional data from a CSV file and preparing it using the SQL node to filter or structure records for pattern discovery. The FP Growth node then applies the Frequent Pattern Growth (FP-Growth) algorithm to identify item combinations that frequently co-occur across transactions.

Once trained, the model produces association rules that highlight relationships between items, aiding in decision-making for product placement, bundling, or targeted marketing. The Print N Rows node displays the resulting patterns for review, while the Spark ML Model Save node saves the trained model for future use in predictive analytics or recommendation workflows.

This workflow offers an efficient approach to mining association rules in large-scale transactional data using distributed Spark ML processing.