Master Informatica Big Data with Key Generator Transformation Tutorial

Master Informatica Big Data with Key Generator Transformation Tutorial

Table of Contents

1. Introduction
2. Understanding Keygen Transformation
3. Setting up the Developer Tool
4. Creating a Mapping in IDQ
5. Reading Data from a Source File
6. Creating a Connection within IDQ
7. Loading Data into a Target Oracle Table
8. Using Expression Transformation
9. Choosing the Key Generation Strategy
10. Running the Keygen Transformation
11. Loading Data into the Target Table
12. Validating and Running the Mapping
13. Checking the Result
14. Conclusion

Introduction

In this article, we will explore the process of creating a keygen transformation in IDQ (Informatica Data Quality). We will discuss the purpose and benefits of using a keygen transformation and how it can be used in conjunction with other transformations in IDQ. We will also provide a step-by-step guide on setting up the developer tool, creating a mapping, reading data from a source file, creating a connection, loading data into a target table, using expression transformation, choosing the key generation strategy, and finally, running the keygen transformation and validating the results.

Understanding Keygen Transformation

The keygen transformation in IDQ is used to generate the same key repeatedly for the same incoming values. It is often used in conjunction with the match transformation, where the match transformation requires input ports generated by the keygen transformation. The keygen transformation creates keys based on specific criteria, such as the combination of first name and last name. The generated keys are then loaded into a target table for further processing.

Setting up the Developer Tool

To begin with, you need to open the IDQ developer tool, also known as the dollar per tool. Once opened, you can start setting up the required components for creating the keygen transformation. This includes reading data from a source file, creating a connection to the target Oracle database, and defining the necessary settings and configurations.

Creating a Mapping in IDQ

To create a keygen transformation, you need to create a mapping in IDQ. This mapping will define the flow of data from the source to the target table. Give a relevant name to your mapping to easily identify it later. In the mapping, you will bring in the source and set it as a read operation. You will also add a target and define the physical data object as a relational data object. This is where you will specify the connection details and choose the target table.

Reading Data from a Source File

Once the mapping is set up, you can configure the source to read data from a source file. This can include files such as CSV, Excel, or any other supported file formats. The source file will contain the necessary data, such as first names and last names, which will be used for generating the keys.

Creating a Connection within IDQ

To load data into a target Oracle table, you need to create a connection within the IDQ developer tool. This connection will establish the link between IDQ and the target database. You will need to provide relevant information such as the database type (Oracle), username, password, and connection string. Once the connection is successfully established, you can proceed with configuring the target settings.

Loading Data into a Target Oracle Table

With the source and target configured, you can now load the generated keys into a target Oracle table. Ensure that the target table is defined properly with the required columns. Pass the columns through the keygen transformation to generate the keys. You can choose the key generation strategy, such as sound X or string, based on your specific requirements. Consider the key length as well, which determines the length of the generated keys.

Using Expression Transformation

To further enhance the functionality and best practices of the keygen transformation, you can use an expression transformation. This transformation helps define the sequence ID, first name, and last name values. It provides more control over the key generation process and allows for customization based on specific requirements.

Choosing the Key Generation Strategy

The keygen transformation offers different key generation strategies, including sound X and NYC, among others. The sound X strategy generates keys based on how the name sounds, which is useful for fuzzy matching. The NYC strategy analyzes vowels throughout a string, making it suitable for fuzzy matching and handling misspellings. Choose the strategy that best fits your data and matching requirements.

Running the Keygen Transformation

Once the keygen transformation and other configurations are in place, you can run the transformation to generate the keys. Use the data viewer to verify the generated keys and ensure they meet the desired criteria. The keygen transformation will create unique keys for each combination of first name and last name, following the specified strategy.

Loading Data into the Target Table

After generating the keys, you can proceed to load the data into the target table. This completes the process of generating and loading the keys based on the keygen transformation. Verify the data in the target table to ensure the keys are correctly generated and associated with the respective records.

Validating and Running the Mapping

It is crucial to validate the mapping before running it. Validation helps identify any errors or issues in the configuration and ensures the mapping is set up correctly. Once the validation is successful, you can proceed to run the mapping. This will execute all the transformations and load the data into the target table.

Checking the Result

After running the mapping, it is important to check the result to ensure that the data and keys are correctly generated and loaded into the target table. Use the data viewer or query the target table to verify the presence of the generated keys and their association with the respective records. This step ensures the accuracy and effectiveness of the keygen transformation.

Conclusion

In this article, we have explored the process of creating a keygen transformation in IDQ. We have discussed the purpose and benefits of using this transformation, as well as provided a step-by-step guide on setting up the developer tool, creating a mapping, reading data from a source file, creating a connection, loading data into a target table, using expression transformation, choosing the key generation strategy, running the transformation, and validating the results. The keygen transformation plays a crucial role in generating consistent keys for matching purposes in IDQ, improving the accuracy and efficiency of data processes.

Highlights

• Keygen transformation allows for generating consistent keys for matching purposes.
• The keygen transformation is often used in conjunction with the match transformation.
• IDQ provides a user-friendly developer tool for configuring and executing keygen transformations.
• Keygen transformation can be customized based on the specific key generation strategy desired.
• Loading data into a target table within IDQ requires a properly configured connection.
• Expression transformation enhances the functionality and customization options of the keygen transformation.
• Validating and running the mapping ensures the accuracy and effectiveness of the keygen transformation.

Frequently Asked Questions (FAQ)

Q: What is the purpose of the keygen transformation in IDQ?

A: The keygen transformation is used to generate consistent keys for matching purposes in IDQ. It allows for the creation of the same key for the same incoming values, which is especially useful in conjunction with the match transformation.

Q: How does the keygen transformation work in IDQ?

A: The keygen transformation works by generating keys based on specific criteria, such as the combination of first name and last name. It follows a chosen key generation strategy, such as sound X or string, to create keys that are consistently generated for the same values.

Q: Can the keygen transformation handle misspellings in the data?

A: Yes, depending on the chosen key generation strategy, such as NYC, the keygen transformation can handle misspellings by analyzing vowels throughout a string. This strategy is useful for fuzzy matching and ensures consistency even in cases of misspelled data.

Q: What role does the expression transformation play in the keygen transformation?

A: The expression transformation enhances the functionality and customization options of the keygen transformation. It allows for defining sequence IDs, first names, last names, and other expressions that can be used in the key generation process.

Q: How can I validate and check the results of the keygen transformation in IDQ?

A: To validate the keygen transformation, you can use the validation feature in IDQ's developer tool. It helps identify any errors or issues in the configuration. To check the results, you can use the data viewer or query the target table to verify the generated keys and their association with the respective records.