Data merging

Purpose

• Join datasets using defined merge keys across any number of sources
• Produce a single, consolidated dataset without requiring manual work in spreadsheets
• Maintain full data lineage so every value can be traced back to its source

Inputs

• Multiple data sources or datasets from earlier nodes in your workflow
• Merge keys that define how records across sources are matched and joined
• Transformation rules that govern how fields are mapped, renamed, or restructured during the merge

Outputs

• Merged datasets combining records from all connected sources into a unified structure
• Merge reports documenting which sources contributed to each record and how they were joined
• Data lineage showing the origin of every field, ready for audit or review

Typical use cases

Data from different sources or time periods needs to be combined into a single data set before it can be processed in the next steps.

• Combining previously unmatched purchase orders with new purchase orders to create a complete reconciliation set
• Creating customer master data by combining data from the CRM and pricing terms via customer IDs