Tally Solutions is a Bangalore-based company that was co-founded by Mr Shyam Sunder Goenka and Mr Bharat Goenka in the year 1986.

This father-son duo came up with this software to help manage the financial transactions in their own company and then given its efficiency launched it for the world.

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In Informatica, an Aggregator Transformation and a normal SQL query serve similar purposes but differ in their approach and functionality: Normal SQL Query: 1. _Executed outside Informatica_: SQL queries are executed directly on the database. 2. _Limited data processing_: SQL queries are designed for set-based operations, not complex data processing. 3. _No data caching_: Results are retrieved directly from the database. 4. _Less flexible_: SQL queries are less flexible in terms of data manipulation and transformation. Aggregator Transformation (in Informatica): 1. _Executed within Informatica_: Aggregator Transformation is a built-in Informatica component. 2. _Advanced data processing_: Enables complex data processing, aggregation, and transformation. 3. _Data caching_: Informatica caches data, reducing database queries and improving performance. 4. _More flexible_: Offers more flexibility in data manipulation, transformation, and aggregation. Key differences: - Execution location (database vs. Informatica) - Data processing capabilities (simple vs. complex) - Data caching (no caching vs. caching) - Flexibility (less flexible vs. more flexible) Choose a normal SQL query for simple, set-based operations, and use Aggregator Transformation for complex data processing, aggregation, and transformation within Informatica workflows. read less

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

Informatica PowerCenter offers various transformations to manipulate and transform data. Here are the main types of transformations: 1. *Active Transformation*: Modifies data in real-time using SQL or Java code. 2. *Aggregator Transformation*: Performs aggregate functions like SUM, AVG, MAX, MIN. 3. *Expression Transformation*: Evaluates expressions and calculates new values. 4. *Filter Transformation*: Filters data based on conditions. 5. *Joiner Transformation*: Combines data from multiple sources. 6. *Lookup Transformation*: Retrieves data from a lookup table. 7. *Mapper Transformation*: Maps data from one format to another. 8. *Rank Transformation*: Assigns a rank to data based on a specified column. 9. *Router Transformation*: Routes data to different targets based on conditions. 10. *Sorter Transformation*: Sorts data in ascending or descending order. 11. *Union Transformation*: Combines data from multiple sources into a single output. 12. *XML Transformation*: Transforms XML data into relational data. 13. *Data Masking Transformation*: Masks sensitive data for security. 14. *Data Validation Transformation*: Validates data against predefined rules. Additionally, Informatica PowerCenter offers advanced transformations like: - *Java Transformation*: Uses Java code to transform data. - *Python Transformation*: Uses Python code to transform data. - *Web Service Transformation*: Calls web services to transform data. - *Cloud Transformation*: Transforms data in cloud-based systems. These transformations help you to manipulate, transform, and prepare data for your business needs. read less

Informatica PowerCenter offers a wide range of transformations to manipulate and transform data. There are approximately 50-60 built-in transformations in Informatica, categorized into: 1. *Active Transformations* (10): e.g., Expression, Filter, Router 2. *Passive Transformations* (20): e.g., Aggregator, Joiner, Lookup 3. *Data Transformation* (10): e.g., Data Masking, Data Validation 4. *XML Transformation* (5): e.g., XML Source, XML Target 5. *Cloud Transformation* (5): e.g., Cloud Source, Cloud Target 6. *Advanced Transformations* (10): e.g., Java, Python, Web Service Note that the exact number of transformations may vary depending on the Informatica version and edition. These transformations enable you to perform various data manipulation tasks, such as data cleansing, aggregation, filtering, and more, to meet your business requirements. read less