Step 3. Map & Transform data
After selecting the source and destination connectors for an Osmos Pipeline the next step is to transform the source data into a format that aligns with the destination connector's schema.
You can clean up and restructure the data using column mapping, QuickFixes, AutoClean, formulas, and single cell edits. If you want to watch a product expert demonstrate this step, click on the demo link below our logo in the top left corner.


Column Mapping Panel

On the left, you will see the column mapping panel. This is where you start, and where you map the relationship between the input (uploaded) and output (cleaned) columns.
Each card in this section represents an output column for your destination connector. You can map one or more input columns to each output column.

Input and Output Columns

After you map an input (uploaded) column to an output (cleaned) column, you will see the data that corresponds to the selected columns to the right of the mapping section.
When you select a different cleaned column in the mapping section, the section to the right will update to display the corresponding data for those columns.

Data Cleanup Panel

To the right of uploaded and cleaned columns, you can access the data cleanup panel. Here you can use tools that help you quickly cleanup the data being sent to the output column you are working on. These tools include QuickFixes, AutoClean, and Formulas. You can get to this panel by clicking on any cell in the destination column.

Getting Started

Step 1: Start in the left Column Mapping panel. Select the column(s) that contain the data for the first output column listed.
Step 2: If you want to cleanup or modify the data in the cleaned (output) column (or resolve errors), click on any cell in the destination column to open the Data Cleanup panel.
Step 3: Review the available options in the Data Cleanup panel and select one that best fits your data cleanup needs for this column:
  1. 1.
    ​QuickFixes - One-click, data-cleanup buttons that allow you to easily clean up your data for the most common scenarios for that data type (i.e. Date, Text, Numeric, etc.).
  2. 2.
    ​AutoClean - Simple to use AI-powered data cleanup that learns and detects a pattern from examples of the clean data.
  3. 3.
    ​Formulas - Spreadsheet style formulas that allow you to clean up your data.
Step 4: Use the toggles at the top of the page to filter for rows with errors and/or rows flagged for review to confirm all issues with your data have been addressed.
Step 5: Once you have repeated Steps 1 through 4 for each of the required output columns (and any of the optional columns you want to include in this Osmos Pipeline), you can proceed to the next step.
Last modified 5d ago