Let's take a look at a few sample prompts to help you get started on your Data Governance and Catalog data transformation journey in CLAIRE GPT.
The sample prompts show how you can use CLAIRE GPT to transform tables and create an ELT pipeline that you can run.
When CLAIRE GPT creates an ELT pipeline, it appears in the form of a mapping in Data Integration. The mapping reads a source and writes the transformed data to a target. To edit, save, and run the mapping, click Open in Data Integration. You can run the mapping using SQL ELT optimization. For more information, see the Data Integration help.
Customers with a feedback score greater than 5
The following image shows the response of CLAIRE GPT to the prompt, displaying customers in the CUSTOMERS table with a feedback score greater than five:
To see the SQL code used to transform the data, expand the Explanation. The following SQL code is used to transform the data:
The following image shows the response of CLAIRE GPT to the prompt, displaying the number of customers created in each year:
Total spend for each customer by joining two tables
The following image shows the response of CLAIRE GPT to the prompt, displaying the total spend for each customer:
Pipeline to load all gold customers
The following image shows the response of CLAIRE GPT to the prompt, creating a pipeline that loads gold customers:
Pipeline to load the number of customers in each city
The following image shows the response of CLAIRE GPT to the prompt, displaying the number of customers in each city:
To create a mapping, use the prompt Create a mapping for the above. The following image shows the response of CLAIRE GPT to the prompt, creating a mapping for customers:
