Flagging Suspicious Data and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, flag suspicious 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

Flagging Suspicious Data

Overview

To maintain data accuracy, records with conflicting information should be flagged. For example, an “Open” ticket should not have a resolution time, and a “Resolved” ticket should have a valid resolution time.

Approach

We use a CASE statement to identify and flag suspicious records:

• “Conflict” → Open tickets with a resolution time
• “Invalid Resolution Time” → Resolved tickets with missing or non-positive resolution time
• “Valid” → All other cases

SQL Query for Flagging Suspicious Data

SELECT 
    Ticket_ID, 
    Ticket_Status, 
    Resolution_Time, 
    CASE 
        WHEN Ticket_Status = 'Open' AND Resolution_Time IS NOT NULL THEN 'Conflict' 
        WHEN Ticket_Status = 'Resolved' AND (Resolution_Time IS NULL OR Resolution_Time <= 0) THEN 'Invalid Resolution Time' 
        ELSE 'Valid' 
    END AS Suspicious_Flag 
FROM {pipeline_name}.sample_csc_data;