Track Ticket Count by Agent and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, check ticket count by agent 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

Ticket Count by Agent

Overview

Tracking the number of tickets resolved by each agent within specific time periods (daily, weekly) helps assess performance, identify workload distribution, and optimize resource allocation.

Approach

We aggregate ticket resolution counts:

• Daily Ticket Count → Grouping by Agent_ID and Ticket_Resolution_Date
• Weekly Ticket Count → Extracting the week number from Ticket_Resolution_Date

SQL Query for Daily Ticket Count

SELECT 
    Agent_ID, 
    Ticket_Resolution_Date, 
    COUNT(Ticket_ID) AS Tickets_Resolved 
FROM {pipeline_name}.sample_csc_data 
WHERE Ticket_Status = 'Resolved'
GROUP BY Agent_ID, Ticket_Resolution_Date 
ORDER BY Ticket_Resolution_Date, Agent_ID;

SQL Query for Weekly Ticket Count

SELECT 
    Agent_ID, 
    EXTRACT(week FROM Ticket_Resolution_Date) AS Resolution_Week, 
    COUNT(Ticket_ID) AS Tickets_Resolved 
FROM {pipeline_name}.sample_csc_data 
WHERE Ticket_Status = 'Resolved' 
GROUP BY Agent_ID, EXTRACT(week FROM Ticket_Resolution_Date) 
ORDER BY Resolution_Week, Agent_ID;