Flatten the nested data and Transforming Data Using Bold Data Hub

In this article, we will demonstrate how to import tables from a CSV file, flatten the nested data 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:
Books

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

Flattening Nested Data

Overview

Nested data structures, such as JSON, can be difficult to analyze directly in SQL because they do not fit neatly into a table format. Flattening these structures converts them into a more accessible table format, making it easier to query and analyze the data.

Approach

We can flatten the nested data by extracting the relevant fields from the JSON structure into separate columns. For example, we can extract values like customer information, ticket details, or support issues into their own columns for easier analysis.

SQL Query for Flattening Nested Data

SELECT  
    title,  
    author,      
    json_extract(metadata, '$.description') AS description, 
    CAST(json_extract(metadata, '$.price') AS DECIMAL) AS price, 
    UNNEST(json_extract(metadata, '$.ages')::int[]) AS age  
FROM {pipeline_name}.books;