1. WHAT IS A NORMALIZER TRANSFORMATION?
2. SCENARIO IMPLEMENTATION 1
3. WHAT ARE LEVELS IN NORMALIZER TRANSFORMATION?
4. WHAT IS THE PURPOSE OF GCID AND GK IN A NORMALIZER TRANSFORMATION?
70.Differences Between Normalizer And Normalizer Transformation.
Normalizer : It is a transformation mainly using for cobol sources. It change the rows into columns and columns into rows.
Normalization : To remove the redundancy and inconsistency.
Normalizer Transformation : can be used to obtain multiple columns from a single row.
71.What Are Main Advantages And Purpose Of Using Normalizer Transformation In Informatica?
Narmalizer Transformation is used mainly with COBOL sources where most of the time data is stored in de-normalized format. Also, Normalizer transformation can be used to create multiple rows from a single row of data.
- Normalizer Transformation read the data from COBOL Sources.
- It support Horizontal Pivot .It is a processing of single input into a multiple output
72. What is a Normalizer transformation?
The normalizer transformation normalizes records from COBOL and relational sources, allowing you to organize the data according to your own needs. A Normalizer transformation can appear anywhere in a data flow when you normalize a relational source. Use a Normalizer transformation instead of the Source Qualifier transformation when you normalize COBOL source. When you drag a COBOL source into the Mapping Designer Workspace, the Normalizer transformation appears, creating input and output ports for every columnsin the source.
74. What are levels in Normalizer transformation?
The VSAM Normalizer transformation is the Source Qualifier for a COBOL source definition. A COBOL can contain multiple-occurring data (Group of columns of same type) and multiple types of records in the same file. Mostly level is for that use. The Normalizer tab defines the structure of the source data. A group of columns might define a record in a COBOL source or it might define a group of multiple-occurring fields in the source.
The column level number identifies groups of columns in the data. Level numbers define a data hierarchy.
Columns in a group have the same level number and display sequentially below a group-level column. A group-level column has a lower level number, and it contains no data.