Create Derived Columns and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, to create derived columns using transformations, and move the cleaned data into the destination database using Bold Data Hub. Follow the step-by-step process below.

Sample Data Source:
Tickets

Step-by-Step Process in Bold Data Hub

Step 1: Open Bold Data Hub

• Click on the Bold Data Hub.

Step 2: Create a New Pipeline

• Click Add Pipeline in the left-side panel.
• Enter the pipeline name and click the tick icon.

Step 3: Choose the Connector

• Select the newly created pipeline and opt for the CSV connector. You can either double-click or click on the Add Template option to include a template.

Step 4: Upload Your CSV File

• Click the “Upload File” button to select and upload your CSV file.

Step 5: Set the Properties

• Copy the file path and paste it into the filePath property field.

Step 6: Save and Choose the Destination

• Click Save, choose the destination, and confirm by clicking the Yes button.

Note: On-Demand Refresh will be triggered when the pipeline is saved. If needed, the pipeline can be scheduled in the Schedules tab.

Step 7: View Logs and Outputs

• Click the pipeline name in the left-side panel and switch to the Logs tab to view logs.

Step 8: Apply Transformations

• Go to the Transform tab and click Add Table.

• Enter the table name to create a transform table for customer satisfaction summary.

Note: The data will initially be transferred to the DuckDB database within the designated {pipeline_name} schema before undergoing transformation for integration into the target databases. As an illustration, in the case of a pipeline named “customer_service_data”, the data will be relocated to the customer_service_data table schema.

Learn more about transformation here

Creating Derived Columns

Overview

Derived columns are new columns created based on existing data. They allow us to gain more granular insights by combining or transforming existing variables. For example, we can combine customer status (new vs. returning) with ticket priority to understand how these two factors influence support ticket trends.

Approach

We can create a new column that combines customer status (e.g., determined by the first ticket date) with ticket priority. This combination can help us analyze the support needs of new versus returning customers and how ticket priority impacts their service experience.

SQL Query for Creating Derived Columns

SELECT *, 
       CASE 
           WHEN CAST(SUBSTR(Customer_ID, 5) AS INTEGER) % 2 = 0 THEN 'Returning' 
           ELSE 'New' 
       END AS Customer_Status,
       CASE 
           WHEN CAST(SUBSTR(Customer_ID, 5) AS INTEGER) % 2 = 0 
           THEN 'Returning - ' || Priority 
           ELSE 'New - ' || Priority 
       END AS Customer_Status_Priority
FROM {pipeline_name}.tickets;