CLAIRE Copilot for Data Integration

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Submit a complete mapping prompt

For best results, provide all details about the mapping in the first prompt that you submit to

CLAIRE Copilot

. To improve the accuracy of the mappings that

CLAIRE Copilot

creates, provide clear and specific details that describe the data flow in the mapping.

To create a complete mapping prompt, use the following guidelines:

Be clear and concise when you describe the use case and mapping details.

Provide all details about source and target objects, source and target connections, and the transformations in the mapping. For each transformation, specify what the transformation does.

Use key words for transformations and functions. For more information about the transformations, transformation capabilities, and functions; see

Transformations

and

Function Reference

Start the prompt by providing source and target details. Then, provide transformation details in the same order that the transformations appear in the mapping.

Specify only one data flow.

CLAIRE Copilot

can't create mappings with multiple data flows.

Create a mapping to filter and route accounts

Let's break down the following prompt:

Create a mapping to extract data from Account table in Salesforce connection and write it to a new table in Snowflake connection. Get the accounts where created year is 2025 and route the accounts in “USA” to one table and the remaining accounts to another table.

The prompt provides the source and target details, including the respective source and target connections. Then, it describes the data flow in the mapping. First, the mapping filters the data by accounts that were created in 2025. Then, the mapping routes the account data to two tables.

The following table lists the key words and how they correspond to mapping elements:

Key words	Mapping element
Account table	Source object in the Source transformation
Salesforce connection	Source connection in the Source transformation
new table	Target object in the Target transformation
Snowflake connection	Target connection in the Target transformation
where created year is 2025	Filter transformation
route the accounts in "USA"	Router transformation

The following image shows each key word in the prompt:

When

CLAIRE Copilot

creates a mapping based on the prompt, it adds the transformations in the following order: Source, Filter, Router, and Target.

Create a mapping to transform account data

Let's break down the following prompt:

Create a mapping to read data from Account table using Salesforce connection and write it to a new table in Snowflake connection. Join the Account and Orders tables on AccountId. Trim all except the last 6 characters from the ID field and filter only for Accounts where Ownership is 2. Find total employees for each Type by counting account IDs. Route the rows where Type is "Customer - Direct" to one table, ones where Type is "Customer - Channel" to a different table, and the remaining to a third table.

The following table lists the key words and how they correspond to transformations in the mapping:

Key word	Transformation
Join	Joiner transformation
Trim	Expression transformation with the LTRIM function
filter	Filter transformation
counting	Aggregator transformation with the COUNT function
Route	Router transformation

The following image shows each key word in the prompt:

When

CLAIRE Copilot

creates a mapping based on the prompt, it adds the transformations in the following order: Source, Joiner, Expression, Filter, Aggregator, Router, and Target.

Combine and extract data from Snowflake

Let's ask

CLAIRE Copilot

to combine and extract data from Snowflake and demonstrate how the results appear in

Data Integration

. We'll specify the source objects, source connection, transformations, target objects, and target connection for

CLAIRE Copilot

to configure in the mapping.

We'll use the following prompt:

Combine and extract data from table EMPLOYEE_DETAILS and EMPLOYEE_SALES using the connection SnowflakeAnalytics2. Lookup the region name from the REGION_MASTER table based on region id. Filter only for employees in North America region. Aggregate the records by summing up the sales for each quarter for each Employee. Then create a full name for each employee and sort the data by Region. Route Q1, Q2, Q3 and Q4 records to new tables Q1_sales, Q2_sales, Q3_sales and Q4_sales respectively using the SnowflakeAnalytics2 connection.

Enter the prompt in the conversation window.

Click

Yes

The following image shows the response from

CLAIRE Copilot

and the created mapping: