Saturday, January 12, 2008

Diffrent terminologies in DW/Informatica

1. What is dimension modeling?
Unlike ER model the dimensional model is very asymmetric with one large central table called as fact table connected to multiple dimension tables .It is also called star schema.

2. What are the active and passive transformations?

An active transformation changes the number of rows that pass through the mapping.
1. Source Qualifier
2. Filter transformation
3. Router transformation
4. Ranker
5. Update strategy
6. Aggregator
7. Advanced External procedure
8. Normalizer
9. Joiner
Passive transformations do not change the number of rows that pass through the mapping.
1. Expressions
2. Lookup
4. Stored procedure
5. External procedure
6. Sequence generator
6. XML Source qualifier

3.Overview of transformations

Source Qualifier Transformation
You can use the Source Qualifier transformation to perform the following tasks:

Join data originating from the same source database.
Filter rows when the PowerCenter Server reads source data.
Specify an outer join rather than the default inner join.
Specify sorted ports.
Create a custom query to issue a special SELECT statement for the PowerCenter Server to read source data.

Aggregator Transformation
The Aggregator transformation allows you to perform aggregate calculations, such as averages and sums. The Aggregator transformation is unlike the Expression transformation, in that you can use the Aggregator transformation to perform calculations on groups. The Expression transformation permits you to perform calculations on a row-by-row basis only.

Expression Transformation
You can use the Expression transformation to calculate values in a single row before you write to the target. For example, you might need to adjust employee salaries, concatenate first and last names, or convert strings to numbers. You can use the Expression transformation to perform any non-aggregate calculations. You can also use the Expression transformation to test conditional statements before you output the results to target tables or other transformations.

Filter Transformation
The Filter transformation allows you to filter rows in a mapping. You pass all the rows from a source transformation through the Filter transformation, and then enter a filter condition for the transformation. All ports in a Filter transformation are input/output, and only rows that meet the condition pass through the Filter transformation.

Joiner Transformation
You can use the Joiner transformation to join source data from two related heterogeneous sources residing in different locations or file systems. Or, you can join data from the same source.

Lookup Transformation
Use a Lookup transformation in a mapping to look up data in a flat file or a relational table, view, or synonym. You can import a lookup definition from any flat file or relational database to which both the PowerCenter Client and Server can connect. You can use multiple Lookup transformations in a mapping.

Normalizer Transformation
Normalization is the process of organizing data. In database terms, this includes creating normalized tables and establishing relationships between those tables according to rules designed to both protect the data and make the database more flexible by eliminating redundancy and inconsistent dependencies.

Rank Transformation
The Rank transformation allows you to select only the top or bottom rank of data. You can use a Rank transformation to return the largest or smallest numeric value in a port or group. You can also use a Rank transformation to return the strings at the top or the bottom of a session sort order. During the session, the PowerCenter Server caches input data until it can perform the rank calculations.

Router Transformation
A Router transformation is similar to a Filter transformation because both transformations allow you to use a condition to test data. A Filter transformation tests data for one condition and drops the rows of data that do not meet the condition. However, a Router transformation tests data for one or more conditions and gives you the option to route rows of data that do not meet any of the conditions to a default output group.

Sequence Generator Transformation
The Sequence Generator transformation generates numeric values. You can use the Sequence Generator to create unique primary key values, replace missing primary keys, or cycle through a sequential range of numbers.

Sorter Transformation
The Sorter transformation allows you to sort data. You can sort data in ascending or descending order according to a specified sort key. You can also configure the Sorter transformation for case-sensitive sorting, and specify whether the output rows should be distinct. The Sorter transformation is an active transformation. It must be connected to the data flow.

Union Transformation
The Union transformation is a multiple input group transformation that you can use to merge data from multiple pipelines or pipeline branches into one pipeline branch. It merges data from multiple sources similar to the UNION ALL SQL statement to combine the results from two or more SQL statements. Similar to the UNION ALL statement, the Union transformation does not remove duplicate rows.

Update Strategy Transformation
When you design your data warehouse, you need to decide what type of information to store in targets. As part of your target table design, you need to determine whether to maintain all the historic data or just the most recent changes.

Stored Procedure Transformation
A Stored Procedure transformation is an important tool for populating and maintaining databases. Database administrators create stored procedures to automate tasks that are too complicated for standard SQL statements.

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