Merging Historical Data and Transforming Data Using Bold Data Hub

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

Sample Data Source:
Sample CSC Data

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

Merging Historical Data

Overview

Creating a long-term service history for customers allows for better understanding of their past issues, service trends, and overall engagement. This helps in improving personalized support and predicting future needs.

Approach

We aggregate historical ticket data per customer to compute:

• Total Tickets → Number of tickets submitted
• Previous Issue Categories → Unique ticket categories the customer has raised
• Last Ticket Date → The most recent ticket creation date
• Customer Lifetime Value → Total spend on support tickets
• Repeat Issue Flag → Identifies if the customer has raised the same issue multiple times

SQL Query for Merging Historical Data

SELECT 
    Customer_ID,
    COUNT(Ticket_ID) AS Total_Tickets,
    STRING_AGG(DISTINCT Ticket_Category, ', ') AS Previous_Issue_Categories,
    MAX(Ticket_Creation_Date) AS Last_Ticket_Date,
    SUM(Ticket_Cost) AS Customer_Lifetime_Value,
    COUNT(*) FILTER (WHERE Ticket_Category IN (
        SELECT Ticket_Category 
        FROM {pipeline_name}.sample_csc_data t2 
        WHERE t2.Customer_ID = {pipeline_name}.sample_csc_data.Customer_ID 
        GROUP BY Ticket_Category 
        HAVING COUNT(*) > 1
    )) > 0 AS Repeat_Issue_Flag
FROM {pipeline_name}.sample_csc_data
GROUP BY Customer_ID;