Question: What do you mean by delta detection in Informatica MDM? What is the process of enabling delta detection?

Delta detection is used to detect new inserts and update existing source datasets in a complete data load process. Delta detection is done on specific columns that can be configured at the stage table level.

Considerations for Using Delta Detection:

Delta detection can be performed by comparing entire data sets or by using date columns. Last modified delta detection is the most efficient because Informatica MDM Hub can simply compare the last modified column of each incoming record to the record's previous last modified date.

With delta detection, you have the option of not including columns in the landing table that are associated with the last_update_date column in the staging table in delta detection. If you include the last_update_date column when you configure delta discovery, and only the last_update_date column changes, the MDM hub will perform a lot of unnecessary delta discovery and loading work.

If you process records by last modified date, do not use the Purge Now feature to compare last modified values ​​(for example, whether the source record's last modified date is earlier than the current system date). to test). Using Now in this manner can produce unexpected results.

Perform delta detection only on columns in the source whose last modified date is not an indicator of actual change. The Informatica MDM Hub stage job compares the entire source record to the latest corresponding record (previous load) in the PRL table. If the cells are different, the records are transferred to a staging table. Delta detection is done using PRL tables.

If delta detection is based on a date column (last_update_date, a system-defined date column, or a custom date column such as date of birth), only records with new date values ​​in the date column (compared to their corresponding records) The maximum date from the RAW table (instead of the PRL table) is displayed and inserted into the staging table.

If delta detection is based on a specific column, including a date column (such as DOB or last_update_date), a record will be detected every time the date in the specified column changes (not the maximum date, but the corresponding value in the PRL table).). in the RAW table) is inserted into the staging table.

If you check the delta of all columns during delta detection, only records with null primary key will be rejected. This is expected behaviour. All other records that fail the delta process are rejected by subsequent processes.

If delta detection is based on last modified date, changes to the last modified date or primary key are detected. Updates to values ​​that do not correspond to the last modified date or that are part of a concatenated primary key are not recognized. When using delta detection with mapped columns, duplicate primary keys are not considered in subsequent processes.

Rejection processing allows you to:

- View all rejected records for a specific staging table related to a batch job

- View all rejected records by day across all staging tables

- Query rejection table based on query filters

Delta Detection Enable for a Staging Table:

Start the Schema Manager.

Selection of the staging table that has be to configure.

Acquire a write lock.

Check the check box to enable delta detection for the table. To see this option, scroll down.

Specify how delta is detected.

Question: What do you mean by full and incremental data load in Informatica MDM?

Incremental data load:

In the case, incremental files from the source are loaded into the MDM landing table daily. New inserts and updates to existing records are determined outside of the MDM process. so there MDM delta detection is not required.

Full data load:

In this case, the source sends a complete data file every day to load the data into the MDM. New inserts and updates to existing records are detected in her MDM as part of the delta discovery process.

Question: Explain about the Cleanse Process in Informatica MDM?

Informatica MDM Hub has a standard set of Cleanse functions that consists of common string manipulation functions, logical operations, data transformation functions, and prebuilt sanitization lists (specific types of sanitization functions). These standard functions can be combined into custom functions to perform data operations specifically tailored to the data cleansing needs of a particular source system. A few custom cleanup functions were created as part of the ORS sample configuration. These functions are in the Cleanse Function tool. These are stored in the Custom, CustomUtilityLibrary, General Processing, IDD Cleanse and Validation Library folders. There are also some sample cleanup lists in the Noise Filters folder. Addition to these custom functions, the sample ORS includes cleansing function libraries (folders) for third-party data quality tools (such as Informatica Address Verification) and third-party data service providers. The functionality of these products can be accessed using special adapters developed with the Informatica MDM Open Cleanse architecture that enable integration of third-party data quality tools.

Question: What do you mean by Auto Merge Jobs in Informatica MDM?

Merge style base objects only: After you run a match job, you can run an automerge job to automatically merge all records that were eligible for automerge during the match process. When you run an automerge job, all matches in the MATCH table that are marked for automerge (Automerge_ind=1) are processed.

Question: What do you mean by control table in Informatica MDM?

Types of system tables in the operational reference store that Informatica MDM Hub automatically creates for base objects. Control tables that are used to support the loading, merging, and unmerging processes. Informatica MDM Hub maintains a record (last modified date and source system identifier) ​​in the corresponding control table for each trusted column in the base object.