Data Transformation > Transform data from the catalog > Sample prompts
  

Sample prompts

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

To find all customers with a feedback score greater than 5, you can enter the following prompt:
Show all customers in @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:
Response from CLAIRE GPT 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:
SELECT
"CUSTOMERS"."FIRST_NAME",
"CUSTOMERS"."GENDER",
"CUSTOMERS"."CUSTOMERID",
"CUSTOMERS"."LAST_NAME",
"CUSTOMERS"."EMAIL",
"CUSTOMERS"."PH_NO",
"CUSTOMERS"."DOB",
"CUSTOMERS"."CREATION_DATE",
"CUSTOMERS"."ACT_STATUS",
"CUSTOMERS"."TYPE",
"CUSTOMERS"."SHOW_PROMOTIONS",
"CUSTOMERS"."PREFERRED_PAYMENT_METHOD",
"CUSTOMERS"."ALTERNATE_CONTACT_NUMBER",
"CUSTOMERS"."OCCUPATION",
"CUSTOMERS"."FEEDBACK_SCORE",
"CUSTOMERS"."REFERENCE",
"CUSTOMERS"."PREFERRED_CONTACT_METHOD",
"CUSTOMERS"."LAST_SEEN_PRODUCT",
"CUSTOMERS"."SUMMARY",
"CUSTOMERS"."RESPONSE"
FROM
"PLATFORMQA"."RETAIL_NEW"."CUSTOMERS" AS "CUSTOMERS"
WHERE
"CUSTOMERS"."FEEDBACK_SCORE" > 5
LIMIT
100

Number of customers created in each year

To find the number of customers created in each year, you can enter the following prompt:
How many customers were created in each year?
The following image shows the response of CLAIRE GPT to the prompt, displaying the number of customers created in each year:
Response from CLAIRE GPT displaying the number of customers created in years 2020, 2022, and 2023 based on the CUSTOMERS table.

Total spend for each customer by joining two tables

To view the total spend for each customer by joining two tables, you can enter the following prompt:
Show me the total spend for each customer by joining @CUSTOMERS and ORDERS
The following image shows the response of CLAIRE GPT to the prompt, displaying the total spend for each customer:
Response from CLAIRE GPT displaying the total spend for each customer after joining the CUSTOMERS and ORDERS tables.

Pipeline to load all gold customers

To create a pipeline to load all gold customers, you can enter the following prompt:
Create a mapping to load all gold customers
The following image shows the response of CLAIRE GPT to the prompt, creating a pipeline that loads gold customers:
Response from CLAIRE GPT displaying a mapping that loads all gold customers using Source, Filter, and Target transformations.

Pipeline to load the number of customers in each city

To find the number of customers in each city, you can enter the following prompt:
Show the number of customers located in each city
The following image shows the response of CLAIRE GPT to the prompt, displaying the number of customers in each city:
Response from CLAIRE GPT displaying the number of customers in each city.
To create a mapping, enter 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:
Response from CLAIRE GPT displaying a mapping that loads the customer data using Source, Joiner, Aggregator, and Target transformations.