Simplified data merge
Description
The Simplified Data Merge activity allows you to combine the output from two or more previous data-producing activities into a single unified dataset. You can control how the merging takes place — whether by combining all columns or only matching columns — and can optionally apply fuzzy matching to handle data discrepancies.
This activity is useful when workflows generate separate datasets that need to be brought together for analysis, reporting, or further transformation.
Use this activity to:
- Combine rows from multiple activities into one dataset
- Merge datasets using exact or approximate (fuzzy) matching
- Consolidate records that share structure or semantics
Use case: A workflow generates two reports: one from e-commerce sales and another from in-store purchases. Using Simplified Data Merge, you can combine both sources into one unified view of total category-wise sales.
Input
| Type | Description |
|---|---|
| Data | Output data from multiple previous activities to be merged. |
Output
| Output Type | Format | Description |
|---|---|---|
| Data | Table | Combined dataset from selected sources. |
Configuration Fields
| Field Name | Description |
|---|---|
| Tables | Select the output tables (activities) that should be merged. You can select two or more activities. |
| Merge Type | Controls how columns are merged: • Merge all columns – Includes all columns from both sources• Merge only common columns – Retains only columns with matching names |
| Use Fuzzy Join | Enable to allow approximate (non-exact) matching between rows based on similarity instead of strict equality. |
| Threshold | Specifies the similarity threshold (typically from 0 to 1) to be used for fuzzy matching. A higher threshold requires more accurate matches. Only rendered when Use Fuzzy Join is enabled. |
Use Fuzzy Join when data from different sources has inconsistencies in values (e.g., typos, partial matches).
Input Data – Activity 1
| category | value |
|---|---|
| Electronics|Mobiles|Computers | 10 |
| Home|Kitchen|Furniture | 20 |
| Home|Kitchen | 20 |
| Furniture|Kitchen|Electronics | 20 |
| Electronics|Mobiles|Furniture | 20 |
Input Data – Activity 2
| category | value |
|---|---|
| Electronics|Mobiles|Computers | 30 |
| Home | 40 |
| Kitchen | 50 |
| Furniture|Kitchen|Electronics | 20 |
| Mobiles|Furniture | 30 |
Sample Configuration
| Field | Value |
|---|---|
| Tables | Activity1, Activity2 |
| Merge Type | Merge all columns |
| Use Fuzzy Join | false |
Output Data
| category | value |
|---|---|
| Electronics|Mobiles|Computers | 10 |
| Home|Kitchen|Furniture | 20 |
| Home|Kitchen | 20 |
| Furniture|Kitchen|Electronics | 20 |
| Electronics|Mobiles|Furniture | 20 |
| Electronics|Mobiles|Computers | 30 |
| Home | 40 |
| Kitchen | 50 |
| Furniture|Kitchen|Electronics | 20 |
| Mobiles|Furniture | 30 |
The merged output retains all rows from both datasets. When using fuzzy join, similar values can be grouped together based on the specified threshold.