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Question: In Dimensional modeling fact table is normalized or denormalized?in case of star schema and incase of snow flake schema?

Answer: In Dimensional modeling, Star Schema: A Single Fact table will be surrounded by a group of Dimensional tables comprise of de- normalized data Snowflake Schema: A Single Fact table will be surrounded by a group of Dimensional tables comprised of normalized dataThe Star Schema (sometimes referenced as star join schema) is the simplest data warehouse schema, consisting of a single "fact table" with a compound primary key, with one segment for each "dimension" and with additional columns of additive, numeric facts.The Star Schema makes multi-dimensional database (MDDB) functionality possible using a traditional relational database. Because relational databases are the most common data management system in organizations today, implementing multi-dimensional views of data using a relational database is very appealing. Even if you are using a specific MDDB solution, its sources likely are relational databases. Another reason for using star schema is its ease of understanding. Fact tables in star schema are mostly in third normal form (3NF), but dimensional tables in de-normalized second normal form (2NF). If you want to normalize dimensional tables, they look like snowflakes (see snowflake schema) and the same problems of relational databases arise - you need complex queries and business users cannot easily understand the meaning of data. Although query performance may be improved by advanced DBMS technology and hardware, highly normalized tables make reporting difficult and applications complex.The Snowflake Schema is a more complex data warehouse model than a star schema, and is a type of star schema. It is called a snowflake schema because the diagram of the schema resembles a snowflake.Snowflake schemas normalize dimensions to eliminate redundancy. That is, the dimension data has been grouped into multiple tables instead of one large table. For example, a product dimension table in a star schema might be normalized into a products table, a Product-category table, and a product-manufacturer table in a snowflake schema. While this saves space, it increases the number of dimension tables and requires more foreign key joins. The result is more complex queries and reduced query performance.
We are using Update Strategy Transformation in mapping how can we know whether insert or update or reject or delete option has been selected during running of sessions in Informatica.
In Designer while creating Update Strategy Transformation uncheck "forward to next transformation". If any rejected rows are there automatically it will be updated to the session log file.
Update or insert files are known by checking the target file or table only.



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Question: In Dimensional modeling fact table is normalized or denormalized?in case of star schema and incase of snow flake schema?
Answer:

In Dimensional modeling, Star Schema: A Single Fact table will be surrounded by a group of Dimensional tables comprise of de- normalized data Snowflake Schema: A Single Fact table will be surrounded by a group of Dimensional tables comprised of normalized dataThe Star Schema (sometimes referenced as star join schema) is the simplest data warehouse schema, consisting of a single "fact table" with a compound primary key, with one segment for each "dimension" and with additional columns of additive, numeric facts.The Star Schema makes multi-dimensional database (MDDB) functionality possible using a traditional relational database. Because relational databases are the most common data management system in organizations today, implementing multi-dimensional views of data using a relational database is very appealing. Even if you are using a specific MDDB solution, its sources likely are relational databases. Another reason for using star schema is its ease of understanding. Fact tables in star schema are mostly in third normal form (3NF), but dimensional tables in de-normalized second normal form (2NF). If you want to normalize dimensional tables, they look like snowflakes (see snowflake schema) and the same problems of relational databases arise - you need complex queries and business users cannot easily understand the meaning of data. Although query performance may be improved by advanced DBMS technology and hardware, highly normalized tables make reporting difficult and applications complex.The Snowflake Schema is a more complex data warehouse model than a star schema, and is a type of star schema. It is called a snowflake schema because the diagram of the schema resembles a snowflake.Snowflake schemas normalize dimensions to eliminate redundancy. That is, the dimension data has been grouped into multiple tables instead of one large table. For example, a product dimension table in a star schema might be normalized into a products table, a Product-category table, and a product-manufacturer table in a snowflake schema. While this saves space, it increases the number of dimension tables and requires more foreign key joins. The result is more complex queries and reduced query performance.
We are using Update Strategy Transformation in mapping how can we know whether insert or update or reject or delete option has been selected during running of sessions in Informatica.
In Designer while creating Update Strategy Transformation uncheck "forward to next transformation". If any rejected rows are there automatically it will be updated to the session log file.
Update or insert files are known by checking the target file or table only. Source: CoolInterview.com



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