Revamp Your Data Strategy with Informatica Cloud

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Revamp Your Data Strategy with Informatica Cloud

Table of Contents:

  1. Introduction
  2. Understanding Update Strategy in IICS/IDMC
  3. Creating the Lookup Object
  4. Mapping the Source and Target Fields
  5. Defining the Update Strategy in Data Driven mode
    1. Insert Operation
    2. Update Operation
    3. Reject Operation
    4. Delete Operation
  6. Executing the Job and Validating Results
  7. Exploring Additional Update Options
    1. Upsert Operation
    2. Delete Operation
  8. Conclusion

Understanding Update Strategy in IICS/IDMC

In this article, we will explore the concept of update strategy in IICS (Informatica Intelligent Cloud Services) and IDMC (Informatica Data Management Cloud). Update strategy refers to the process of determining how data should be handled when performing updates in an ETL (Extract, Transform, Load) workflow. It involves making decisions Based on the existence of data in the target system, such as whether to insert new records, update existing records, reject invalid records, or delete unwanted records.

Creating the Lookup Object

Before configuring the update strategy, we need to Create a lookup object to compare the data in the target table with the source data. This lookup object will help us determine whether a Record already exists in the target or if it needs to be updated. To create the lookup object, we will define the lookup condition and configure the return fields.

Mapping the Source and Target Fields

Next, we need to map the fields from the source table to the target table. This involves connecting the source and target objects in the mapping and defining the field mappings. It is important to ensure that all the necessary fields are mapped correctly to avoid any data inconsistencies.

Defining the Update Strategy in Data Driven mode

To define the update strategy, we will use the data driven mode in the target properties. This mode allows us to specify the operations that need to be performed based on certain conditions. We will cover four main operations: insert, update, reject, and delete.

  • Insert Operation: We will configure the insert operation to insert new records into the target table when a record does not already exist in the target.
  • Update Operation: The update operation will update existing records in the target table when a match is found between the source and target data.
  • Reject Operation: The reject operation will handle records that do not meet certain criteria or fail to match the lookup condition. These records will be rejected and not loaded into the target.
  • Delete Operation: The delete operation will remove unwanted records from the target table based on certain conditions. This operation can be useful for maintaining data integrity.

Executing the Job and Validating Results

After configuring the update strategy, we can run the job and validate the results. We will check if the insert, update, reject, and delete operations are working as expected. This step is crucial to ensure that the update strategy is performing correctly and handling the data according to the specified criteria.

Exploring Additional Update Options

In addition to the basic update operations, IICS and IDMC offer additional options for handling updates in an ETL workflow. We will explore two additional options: upsert and delete.

  • Upsert Operation: The upsert operation combines the insert and update operations into a single step. It will insert new records and update existing records based on certain conditions, making the process more efficient.
  • Delete Operation: The delete operation allows us to directly remove specific records from the target table based on predefined criteria. This operation can be useful for maintaining data quality and managing unwanted data.

Conclusion

Update strategy is an essential aspect of data integration and ETL workflows. With the capabilities of IICS and IDMC, handling data updates becomes more flexible and efficient. By understanding and utilizing the update strategy options, developers can ensure the accuracy and effectiveness of their data integration processes.

Highlights:

  • Understanding the concept of update strategy in IICS/IDMC
  • Creating a lookup object to compare source and target data
  • Mapping the source and target fields for data synchronization
  • Configuring the update strategy in data driven mode for insert, update, reject, and delete operations
  • Executing the job and validating the results
  • Exploring additional update options such as upsert and delete operations

FAQ:

Q: What is an update strategy? A: Update strategy refers to the process of determining how data should be handled in an ETL workflow, specifically when performing updates. It involves making decisions based on the existence of data in the target system, such as whether to insert new records, update existing records, reject invalid records, or delete unwanted records.

Q: How is the update strategy implemented in IICS/IDMC? A: In IICS and IDMC, the update strategy is implemented using the data driven mode in the target properties. This allows developers to configure insert, update, reject, and delete operations based on specific conditions.

Q: What is the purpose of a lookup object in the update strategy? A: The lookup object is used to compare the data in the target table with the source data. It helps determine whether a record already exists in the target or if it needs to be updated, enabling accurate data synchronization.

Q: Can I perform upsert and delete operations in IICS/IDMC? A: Yes, IICS and IDMC offer additional options for handling updates. Upsert operations combine insert and update operations into a single step, while delete operations allow for the direct removal of specific records from the target table.

Q: Why is validating the results of the update strategy important? A: Validating the results ensures that the update strategy is working as expected and handling the data according to the specified criteria. It helps identify any issues or discrepancies in the data integration process.

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