Lecture on Using Normalized Transformation in Informatica Cloud

Introduction

• Normalized Transformation: Active and connected transformation in Informatica Cloud.
• Purpose: Transforms one incoming row with multiple occurrences into multiple rows.
• Outputs: Generates GK, GCI ID, and GK FS fields.

Example and Process

• Source Example:
  • Columns: B name, match1, match2, match3, match4.
  • Example Records: R S (match1: 30 runs, match2: 15 runs, match3: 0o, match4: 51 runs).
• Target Example:
  • Converts columns to rows, creating multiple rows for each B name and runs.

Step-by-step Guide

1. Creating Mapping in ISS:

   • Go to 'New' > 'Mapping' > 'Create mapping'.
   • Select the source fields (e.g., matchman name, match1, match2, match3, match4).

2. Adding Normalizer Transformation:

   • Maximize to view incoming fields to be normalized.
   • Select 'matchman name' as an occurrence of 1.
   • Create new field with occurrence 4 (for four matches).
   • Autocreates GCI ID and GK run fields.

3. Field Mapping:

   • Map fields: matchman name, match1 to run1, match2 to run2, match3 to run3, match4 to run4.
   • Four fields created in field mapping due to occurrence of 4 times.

4. Setting Target:

   • Configure target connection as flat file (CSV).
   • Include/exclude GCI and GK fields as needed.

5. Saving and Running the Mapping:

   • Save the mapping.
   • Create and configure 'Mapping Task'.
   • Provide name, select at run-time (WR), and finish.
   • Run the mapping task.

Output Results

• Job Execution:
  • From source: 3 records.
  • To target: 14 records loaded.
• Reviewing Target File:
  • Example records: Four records for 'R' (0o, 51, 30, 51 runs) and subsequent sequences for other records.
  • Sequence generation: Records are sequenced (e.g., 1, 2, 3, 4 for four occurrences).
  • Column to row transformation results in sequence numbers for occurrences.

Conclusion

• Column to row conversion is efficiently done using the normalizer transformation in Informatica Cloud.
• Ensures data normalization with generated sequence IDs and fields.

Thank you!