1. Home
  2. Previous Releases
  3. Latest Release
  4. Guides
  5. Concepts
  6. Tools
  7. References
    1. Apache Spark
    2. Built-in Functions
    3. Incorta Component SDK
    4. Incorta Connector SDK
    5. Connectors
    6. Data Studio
      1. Enabling the Data Studio
      2. Aggregation Recipe
      3. Change Type Recipe
      4. Data Quality Recipe
      5. Filter Recipe
      6. Formula Recipe
      7. Fuzzy Join Recipe
      8. Join Recipe
      9. LLM Recipe
      10. Python Recipe
      11. Rename Recipe
      12. Sample Recipe
      13. Save MV Recipee
      14. Select Recipe
      15. Sort Recipe
      16. Split Recipe
      17. SQL Recipe
      18. Union Recipe
      19. Unpivot Recipe
    7. Public API
    8. Security
    9. SQL Interface
    10. Visualizations
    11. Other
  8. Integrations
  9. Data applications
  10. Releases
  11. Incorta Premium

References → Union Recipe

Unioning data combines two datasets into one, providing a comprehensive view that enhances analysis and decision-making. This process reveals patterns and trends not visible in isolated data, improves data quality, and supports better forecasting and reporting, leading to more informed strategic outputs.

Configuration

Version 2024.7.1 and earlier

ConfigurationDescription
Recipe NameA freeform name of how a user would like to name a recipe
Top InputInput the data you want to show at the top of a dataset. The schema defined by the first dataset will be applied to the second dataset.
Bottom InputInput the data to be merged into the top input.
Note

Columns in a union are resolved by position. This means columns appearing in a different order between datasets will not be correctly mapped. Additionally, if there is a mismatch in the number of columns between the two input datasets, the recipe will fail.

Version 2024.7.2 and later

ConfigurationDescription
Recipe NameA freeform name of how a user would like to name a recipe
Top InputInput the data you want to show at the top of a dataset. The schema defined by the first dataset will be applied to the second dataset.
Bottom InputInput the data to be merged into the top input.
Union TypeSelect if the Union should or should not de-duplicate non-unique records
Union MethodSelect if columns should be unioned based on:
  ●   Postition - (e.g. Column 1 from top input will union with Column 1 from bottom input)
  ●   Name - (e.g. Union will map to common names between top and bottom inputs)
Handle Missing ColumnsSelect the behavior of the output when the top and bottom datasets have a descrepancy in a number of columns (union on position), or in names (union on name).
Handle Type MismatchIf enabled, the union automatically casts columns to a compatible data type if there is a mismatch. For example, unioning a string column and a numeric column will result in the column being output as string. This will eliminate the creation of null values on the output.