Connected
Lookup and Unconnected Lookup
Connected
Lookup
|
Unconnected
Lookup
|
Receives
input values directly from the pipeline.
|
Receives
input values from the result of a :LKP expression in another transformation.
|
We
can use a dynamic or static cache.
|
We
can use a static cache.
|
Cache
includes all lookup columns used in the mapping.
|
Cache
includes all lookup/output ports in the lookup condition and the
lookup/return port.
|
If
there is no match for the lookup condition, the Power Center Server returns
the default value for all output ports.
|
If
there is no match for the lookup condition, the Power Center Server returns
NULL.
|
If
there is a match for the lookup condition, the Power Center Server returns
the result of the lookup condition for all lookup/output ports.
|
If
there is a match for the lookup condition, the Power Center Server returns
the result of the lookup condition into the return port.
|
Pass
multiple output values to another transformation.
|
Pass
one output value to another transformation.
|
Supports
user-defined default values
|
Does
not support user-defined default values.
|
Cache
Comparison
Persistence
and Dynamic Caches
Dynamic
1) When you use a dynamic cache, the Informatica Server updates the lookup cache as it passes rows to the target.
1) When you use a dynamic cache, the Informatica Server updates the lookup cache as it passes rows to the target.
2) In Dynamic, we can
update catch will new data also.
3) Dynamic cache, Not
Reusable.
(When we need updated
cache data, That only we need Dynamic Cache)
Persistent
1) A Lookup transformation to use a non-persistent or persistent cache. The PowerCenter Server saves or deletes lookup cache files after a successful session based on the Lookup Cache Persistent property.
1) A Lookup transformation to use a non-persistent or persistent cache. The PowerCenter Server saves or deletes lookup cache files after a successful session based on the Lookup Cache Persistent property.
2) Persistent, we are
not able to update the catch with new data.
3) Persistent catch
is Reusable.
(When we need
previous cache data, that only we need Persistent Cache)
Star
Schema And Snow Flake Schema
Informatica - Transformations
In Informatica, Transformations
help to transform the source data according to the requirements of target
system and it ensures the quality of the data being loaded into target.
Transformations are of two types: Active and Passive.
Active Transformation
An active transformation can change the number of rows that pass through it from source to target. (i.e) It eliminates rows that do not meet the condition in transformation.
Passive Transformation
A passive transformation does not change the number of rows that pass through it (i.e) It passes all rows through the transformation.
Transformations can be Connected or Unconnected.
Connected Transformation
Connected transformation is connected to other transformations or directly to target table in the mapping.
Unconnected Transformation
An unconnected transformation is not connected to other transformations in the mapping. It is called within another transformation, and returns a value to that transformation.
Transformations are of two types: Active and Passive.
Active Transformation
An active transformation can change the number of rows that pass through it from source to target. (i.e) It eliminates rows that do not meet the condition in transformation.
Passive Transformation
A passive transformation does not change the number of rows that pass through it (i.e) It passes all rows through the transformation.
Transformations can be Connected or Unconnected.
Connected Transformation
Connected transformation is connected to other transformations or directly to target table in the mapping.
Unconnected Transformation
An unconnected transformation is not connected to other transformations in the mapping. It is called within another transformation, and returns a value to that transformation.
Following are the list of Transformations available in
Informatica:
Aggregator Transformation
Expression Transformation
Filter Transformation
Joiner Transformation
Lookup Transformation
Normalizer Transformation
Rank Transformation
Router Transformation
Sequence Generator Transformation
Stored Procedure Transformation
Sorter Transformation
Update Strategy Transformation
XML Source Qualifier Transformation
In the following pages, we will explain all the above Informatica Transformations and their significances in the ETL process in detail.
Expression Transformation
Filter Transformation
Joiner Transformation
Lookup Transformation
Normalizer Transformation
Rank Transformation
Router Transformation
Sequence Generator Transformation
Stored Procedure Transformation
Sorter Transformation
Update Strategy Transformation
XML Source Qualifier Transformation
In the following pages, we will explain all the above Informatica Transformations and their significances in the ETL process in detail.
==============================================================================
Aggregator Transformation
Aggregator
transformation is an Active and Connected transformation.
This transformation is useful to perform calculations such as averages and sums (mainly to perform calculations on multiple rows or groups).
For example, to calculate total of daily sales or to calculate average of monthly or yearly sales. Aggregate functions such as AVG, FIRST, COUNT, PERCENTILE, MAX, SUM etc. can be used in aggregate transformation.
This transformation is useful to perform calculations such as averages and sums (mainly to perform calculations on multiple rows or groups).
For example, to calculate total of daily sales or to calculate average of monthly or yearly sales. Aggregate functions such as AVG, FIRST, COUNT, PERCENTILE, MAX, SUM etc. can be used in aggregate transformation.
==============================================================================
Expression Transformation
Expression transformation
is a Passive and Connected transformation.
This can be used to calculate values in a single row before writing to the target.
For example, to calculate discount of each product
or to concatenate first and last names
or to convert date to a string field.
This can be used to calculate values in a single row before writing to the target.
For example, to calculate discount of each product
or to concatenate first and last names
or to convert date to a string field.
==============================================================================
Filter Transformation
Filter transformation is an Active and Connected transformation.
This can be used to filter rows in a mapping that do not meet the condition.
For example,
To know all the employees who are working in Department 10 or
To find out the products that falls between the rate category $500 and $1000.
Filter transformation is an Active and Connected transformation.
This can be used to filter rows in a mapping that do not meet the condition.
For example,
To know all the employees who are working in Department 10 or
To find out the products that falls between the rate category $500 and $1000.
==============================================================================
Joiner Transformation
Joiner
Transformation is an Active and Connected transformation. This can be used to
join two sources coming from two different locations or from same location. For
example, to join a flat file and a relational source or to join two flat files
or to join a relational source and a XML source.
In order to join two sources, there must be at least one matching port. While joining two sources it is a must to specify one source as master and the other as detail.
In order to join two sources, there must be at least one matching port. While joining two sources it is a must to specify one source as master and the other as detail.
The Joiner transformation supports the following types of joins:
1)Normal
2)Master Outer
3)Detail Outer
4)Full Outer
Normal join discards all the rows of data from the master and detail source that do not match, based on the condition.
Master outer join discards all the unmatched rows from the master source and keeps all the rows from the detail source and the matching rows from the master source.
Detail outer join keeps all rows of data from the master source and the matching rows from the detail source. It discards the unmatched rows from the detail source.
Full outer join keeps all rows of data from both the master and detail sources.
1)Normal
2)Master Outer
3)Detail Outer
4)Full Outer
Normal join discards all the rows of data from the master and detail source that do not match, based on the condition.
Master outer join discards all the unmatched rows from the master source and keeps all the rows from the detail source and the matching rows from the master source.
Detail outer join keeps all rows of data from the master source and the matching rows from the detail source. It discards the unmatched rows from the detail source.
Full outer join keeps all rows of data from both the master and detail sources.
==============================================================================
Lookup
transformation
Lookup
transformation is Passive and it can be both Connected and UnConnected as
well. It is used to look up data in a relational table, view, or synonym.
Lookup definition can be imported either from source or from target tables.
For example, if we want to retrieve all the sales of a product with an ID 10 and assume that the sales data resides in another table. Here instead of using the sales table as one more source, use Lookup transformation to lookup the data for the product, with ID 10 in sales table.
For example, if we want to retrieve all the sales of a product with an ID 10 and assume that the sales data resides in another table. Here instead of using the sales table as one more source, use Lookup transformation to lookup the data for the product, with ID 10 in sales table.
Connected lookup receives input values directly from mapping
pipeline whereas
Unconnected lookup receives values from: LKP expression from another transformation.
Connected lookup returns multiple columns from the same row whereas
Unconnected lookup has one return port and returns one column from each row.
Connected lookup supports user-defined default values whereas
Unconnected lookup does not support user defined values.
Unconnected lookup receives values from: LKP expression from another transformation.
Connected lookup returns multiple columns from the same row whereas
Unconnected lookup has one return port and returns one column from each row.
Connected lookup supports user-defined default values whereas
Unconnected lookup does not support user defined values.
==============================================================================
Normalizer Transformation
Normalizer
Transformation is an Active and Connected transformation.
It is used mainly with COBOL sources where most of the time data is stored in de-normalized format.
Also, Normalizer transformation can be used to create multiple rows from a single row of data.
It is used mainly with COBOL sources where most of the time data is stored in de-normalized format.
Also, Normalizer transformation can be used to create multiple rows from a single row of data.
==============================================================================
Rank Transformation
Rank
transformation is an Active and Connected transformation.
It is used to select the top or bottom rank of data.
For example,
To select top 10 Regions where the sales volume was very high
or
To select 10 lowest priced products.
It is used to select the top or bottom rank of data.
For example,
To select top 10 Regions where the sales volume was very high
or
To select 10 lowest priced products.
==============================================================================
Router Transformation
Router
is an Active and Connected transformation. It is similar to filter
transformation.
The only difference is, filter transformation drops the data that do not meet the condition whereas router has an option to capture the data that do not meet the condition. It is useful to test multiple conditions.
It has input, output and default groups.
For example, if we want to filter data like where State=Michigan, State=California, State=New York and all other States. It’s easy to route data to different tables.
The only difference is, filter transformation drops the data that do not meet the condition whereas router has an option to capture the data that do not meet the condition. It is useful to test multiple conditions.
It has input, output and default groups.
For example, if we want to filter data like where State=Michigan, State=California, State=New York and all other States. It’s easy to route data to different tables.
==============================================================================
Sequence Generator Transformation
Sequence Generator transformation is a Passive and Connected transformation. It is used to create unique primary key values or cycle through a sequential range of numbers or to replace missing keys.
It has two output ports to connect transformations. By default it has two fields CURRVAL and NEXTVAL (You cannot add ports to this transformation).
NEXTVAL port generates a sequence of numbers by connecting it to a transformation or target. CURRVAL is the NEXTVAL value plus one or NEXTVAL plus the Increment By value.
Sequence Generator transformation is a Passive and Connected transformation. It is used to create unique primary key values or cycle through a sequential range of numbers or to replace missing keys.
It has two output ports to connect transformations. By default it has two fields CURRVAL and NEXTVAL (You cannot add ports to this transformation).
NEXTVAL port generates a sequence of numbers by connecting it to a transformation or target. CURRVAL is the NEXTVAL value plus one or NEXTVAL plus the Increment By value.
==============================================================================
Sorter Transformation
Sorter transformation is a Connected and an Active transformation.
It allows sorting data either in ascending or descending order according to a specified field.
Also used to configure for case-sensitive sorting, and specify whether the output rows should be distinct.
Sorter transformation is a Connected and an Active transformation.
It allows sorting data either in ascending or descending order according to a specified field.
Also used to configure for case-sensitive sorting, and specify whether the output rows should be distinct.
==============================================================================
Source Qualifier Transformation
Source Qualifier transformation is an Active and Connected transformation. When adding a relational or a flat file source definition to a mapping, it is must to connect it to a Source Qualifier transformation.
The Source Qualifier performs the various tasks such as
Overriding Default SQL query,
Filtering records;
join data from two or more tables etc.
Source Qualifier transformation is an Active and Connected transformation. When adding a relational or a flat file source definition to a mapping, it is must to connect it to a Source Qualifier transformation.
The Source Qualifier performs the various tasks such as
Overriding Default SQL query,
Filtering records;
join data from two or more tables etc.
==============================================================================
Stored Procedure Transformation
Stored
Procedure transformation is a Passive and Connected & Unconnected transformation.
It is useful to automate time-consuming tasks and it is also used in error
handling, to drop and recreate indexes and to determine the space in database,
a specialized calculation etc.
The stored procedure must exist in the database before creating a Stored Procedure transformation, and the stored procedure can exist in a source, target, or any database with a valid connection to the Informatica Server. Stored Procedure is an executable script with SQL statements and control statements, user-defined variables and conditional statements.
The stored procedure must exist in the database before creating a Stored Procedure transformation, and the stored procedure can exist in a source, target, or any database with a valid connection to the Informatica Server. Stored Procedure is an executable script with SQL statements and control statements, user-defined variables and conditional statements.
==============================================================================
Update Strategy Transformation
Update
strategy transformation is an Active and Connected transformation.
It is used to update data in target table, either to maintain history of data or recent changes.
You can specify how to treat source rows in table, insert, update, delete or data driven.
It is used to update data in target table, either to maintain history of data or recent changes.
You can specify how to treat source rows in table, insert, update, delete or data driven.
==============================================================================
XML Source Qualifier Transformation
XML Source Qualifier is a Passive and Connected transformation.
XML Source Qualifier is used only with an XML source definition.
It represents the data elements that the Informatica Server reads when it executes a session with XML sources.
XML Source Qualifier is a Passive and Connected transformation.
XML Source Qualifier is used only with an XML source definition.
It represents the data elements that the Informatica Server reads when it executes a session with XML sources.
==============================================================================
No comments:
Post a Comment