How to Pass Data Between Mapping Tasks in Informatica Cloud

How to Pass Data Between Mapping Tasks in Informatica Cloud

Table of Contents

1. Introduction
2. Passing Values from One Mapping Task to Another Using In-Out Parameters
   1. Scenario 1: Getting the Maximum Date Record
   2. Scenario 2: Getting the Maximum Salary Record
3. Setting Up the Mapping Task for Scenario 1
   1. Configuring the Source and Target
   2. Converting Data Types with Expressions
   3. Mapping the Target
   4. Creating the In-Out Parameter for Maximum Date
   5. Initializing and Passing the In-Out Parameter
   6. Storing the In-Out Parameter Value in a Dummy Target
   7. Creating the Mapping Task and Runtime Environment
   8. Testing and Validating the Mapping Task
4. Setting Up the Mapping Task for Scenario 2
   1. Copying and Modifying the Previous Mapping Task
   2. Creating the In-Out Parameter for Maximum Salary
   3. Mapping the Filter Condition for Maximum Salary
   4. Testing and Validating the Mapping Task
5. Creating the Task Flow for Both Scenarios
   1. Configuring the Data Task for Maximum Date
   2. Configuring the Data Task for Maximum Salary
   3. Creating the Task Flow
6. Running the Task Flow and Verifying Results
   1. Analyzing the Job Execution and Validating the Results
   2. Modifying Data to Test Updates in Maximum Salary
   3. Running the Job Again and Verifying Results

Passing Values from One Mapping Task to Another Using In-Out Parameters

In complex data integration scenarios, it is often necessary to pass values from one mapping task to another. This allows for the flow of information and ensures accurate data processing. In this article, we will explore two scenarios for passing values: getting the maximum date record and getting the maximum salary record. Both examples demonstrate how to pass values using in-out parameters.

Scenario 1: Getting the Maximum Date Record

In the first scenario, we will focus on retrieving the maximum date record. We will use a flat file source and an Oracle target. The mapping task will involve converting data types, mapping the target, and creating an in-out parameter for the maximum date value. The goal is to load only the record with the maximum date into the target table.

Scenario 2: Getting the Maximum Salary Record

The Second scenario revolves around obtaining the record with the maximum salary. Similar to the previous scenario, we will utilize a flat file source and an Oracle target. The process will involve converting data types, mapping the filter condition for maximum salary, and creating an in-out parameter specifically for the maximum salary value. The objective is to load only the record with the highest salary into the target table.

Setting Up the Mapping Task for Scenario 1

To begin, we need to configure the mapping task for scenario 1, which involves getting the maximum date record. Follow the steps below:

1. Start by setting up the source and target. In this case, we will use a flat file as the source and an Oracle table as the target.
2. Convert the data types using expressions. Ensure that the Create date field is converted to a date and the salary field is converted to a decimal.
3. Map the target by connecting the expression fields to the corresponding target columns.
4. Create an in-out parameter for the maximum date, which will serve as the input value for the following mapping task.
5. Initialize and pass the in-out parameter by using built-in functions.
6. Store the in-out parameter value in a dummy target for runtime purposes.
7. Create the mapping task for empty task 1, specifying the runtime environment.
8. Test and validate the mapping task to ensure accurate execution.

Setting Up the Mapping Task for Scenario 2

Next, we will set up the mapping task for scenario 2, which involves getting the maximum salary record. Here's how to do it:

1. Copy the mapping from the previous scenario and paste it into a new mapping. Adjust the mapping name accordingly.
2. Modify the in-out parameter for maximum salary.
3. Map the filter condition for the maximum salary, ensuring it matches the parameter passed by the previous mapping.
4. Test and validate the mapping task to confirm accurate execution.

Creating the Task Flow for Both Scenarios

Once the mapping tasks for both scenarios are set up, we can create a task flow to connect them. Follow the steps below:

1. Configure the data task for the maximum date, connecting it to the empty task for maximum date.
2. Configure the data task for the maximum salary, connecting it to the empty task for maximum salary.
3. Create the task flow, ensuring a logical sequence of execution.

Running the Task Flow and Verifying Results

With the task flow created, it's time to run the job and examine the results. Follow these steps:

1. Analyze the job execution to ensure all steps are completed successfully.
2. Verify the results by checking the target tables for records that match the maximum date and maximum salary conditions.
3. Modify data to test updates in the maximum salary.
4. Run the job again and verify the updated results.

By following these steps, You can successfully pass values from one mapping task to another using in-out parameters. This approach allows for flexible and accurate data processing in complex integration scenarios.

Highlights

1. Two scenarios for passing values: maximum date record and maximum salary record
2. Configuring the mapping tasks for each scenario
3. Creating in-out parameters and initializing them
4. Mapping filters and conditions Based on the passed parameters
5. Creating the task flow to connect the mapping tasks
6. Running the job and verifying the results

FAQs

Q: Can I use this method for other data types, such as text or boolean? A: Yes, the method of passing values using in-out parameters can be applied to various data types, including text and boolean.

Q: Can I pass multiple values to the next mapping task? A: Yes, you can pass multiple values by creating additional in-out parameters and mapping them accordingly.

Q: Is it possible to have more than two mapping tasks in a task flow? A: Absolutely. Task flows can include any number of mapping tasks, depending on the complexity of the data integration scenario.