Skip to main content

What is the difference between snow flake and star schema



                                                                                                                                                                      
Scenario:
What is the difference between snow flake and star schema


Solution:

Star Schema
Snow Flake Schema
The star schema is the simplest data warehouse scheme.
Snowflake schema is a more complex data warehouse model than a star schema.
In star schema each of the dimensions is represented in a single table .It should not have any hierarchies between dims.
In snow flake schema at least one hierarchy should exists between dimension tables.
It contains a fact table surrounded by dimension tables. If the dimensions are de-normalized, we say it is a star schema design.
It contains a fact table surrounded by dimension tables. If a dimension is normalized, we say it is a snow flaked design.
In star schema only one join establishes the relationship between the fact table and any one of the dimension tables.
In snow flake schema since there is relationship between the dimensions tables it has to do many joins to fetch the data.
A star schema optimizes the performance by keeping queries simple and providing fast response time. All the information about the each level is stored in one row.
Snowflake schemas normalize dimensions to eliminated redundancy. The result is more complex queries and reduced query performance.
It is called a star schema because the diagram resembles a star.
It is called a snowflake schema because the diagram resembles a snowflake.


Comments

Post a Comment

Popular posts from this blog

CMN_1650 A duplicate row was attempted to be inserted into a dynamic lookup cache Dynamic lookup error.

Scenario: I have 2 ports going through a dynamic lookup, and then to a router. In the router it is a simple case of inserting new target rows (NewRowLookup=1) or rejecting existing rows (NewRowLookup=0). However, when I run the session I'm getting the error: "CMN_1650 A duplicate row was attempted to be inserted into a dynamic lookup cache Dynamic lookup error. The dynamic lookup cache only supports unique condition keys." I thought that I was bringing through duplicate values so I put a distinct on the SQ. There is also a not null filter on both ports. However, whilst investigating the initial error that is logged for a specific pair of values from the source, there is only 1 set of them (no duplicates). The pair exists on the target so surely should just return from the dynamic lookup newrowlookup=0. Is this some kind of persistent data in the cache that is causing this to think that it is duplicate data? I haven't got the persistent cache or...
Post a Comment
Read more

SQL Transformation with examples

============================================================================================= SQL Transformation with examples   Use : SQL Transformation is a connected transformation used to process SQL queries in the midstream of a pipeline . We can insert, update, delete and retrieve rows from the database at run time using the SQL transformation. Use SQL transformation in script mode to run DDL (data definition language) statements like creating or dropping the tables. The following SQL statements can be used in the SQL transformation. Data Definition Statements (CREATE, ALTER, DROP, TRUNCATE, RENAME) DATA MANIPULATION statements (INSERT, UPDATE, DELETE, MERGE) DATA Retrieval Statement (SELECT) DATA Control Language Statements (GRANT, REVOKE) Transaction Control Statements (COMMIT, ROLLBACK) Scenario: Let’s say we want to create a temporary table in mapping while workflow is running for some intermediate calculation. We can use SQL transformat...
1 comment
Read more

Informatica Quiz: Set 1

                                                                                                                                                                    ...
2 comments
Read more