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Informatica > Learn Informatica powercenter (ETL) > How do I convert the normalizer transformation of Informatica...
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How do I convert the normalizer transformation of Informatica into an SQL query?

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Learn Informatica powercenter (ETL) +1 Tally Shoper 9

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Converting an Informatica Normalizer Transformation to an SQL query involves rewriting the transformation logic using SQL syntax. Here's a general outline: 1. *Identify the Normalizer Transformation logic*: Understand the input fields, output fields, and the normalization rules applied. 2. *Determine...
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Converting an Informatica Normalizer Transformation to an SQL query involves rewriting the transformation logic using SQL syntax. Here's a general outline: 1. *Identify the Normalizer Transformation logic*: Understand the input fields, output fields, and the normalization rules applied. 2. *Determine the equivalent SQL operations*: Map the Normalizer Transformation logic to corresponding SQL operations, such as: - UNPIVOT or PIVOT for column normalization - CASE statements or DECODE functions for conditional logic - STRING functions (e.g., SUBSTR, CONCAT) for text manipulation 3. *Write the SQL query*: - Use a SELECT statement to specify the output fields - Apply the equivalent SQL operations to normalize the data - Use FROM and JOIN clauses to specify the input tables and relationships Example: Informatica Normalizer Transformation: - Input: `NAME` (single column with multiple values separated by commas) - Output: `NAME1`, `NAME2`, ... (multiple columns, each with a single value) - Normalization rule: Split `NAME` into separate columns using commas as separators Equivalent SQL query: ``` SELECT REGEXP_SUBSTR(NAME, '[^,]+', 1, 1) AS NAME1, REGEXP_SUBSTR(NAME, '[^,]+', 1, 2) AS NAME2, ... FROM INPUT_TABLE; ``` Note: The SQL syntax may vary depending on the database management system (DBMS) you're using. Keep in mind that complex Normalizer Transformations might require more intricate SQL queries, potentially involving multiple steps or Common Table Expressions (CTEs). read less
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