This is Kashif, explaining informatica in detail level for freshers. I hope this informatica tuturial will be helpful.
Informatica
Informatica is a powerful ETL tool from Informatica Corporation, a leading
provider of enterprise data integration software and ETL softwares.
The important Informatica
Components are:
- Power Exchange
- Power Center
- Power Center Connect
- Power Exchange
- Power Channel
- Metadata Exchange
- Power Analyzer
- Super Glue
In Informatica, all the Metadata information about source systems, target
systems and transformations are stored in the Informatica repository.
Informatica's Power Center Client and Repository Server access this repository
to store and retrieve metadata.
Source and Target:
Consider a Bank that has got many branches throughout the world. In each branch data may be stored in different source systems like oracle, sql server, terradata, etc. When the Bank decides to integrate its data from several sources for its management decisions, it may choose one or more systems like oracle, sql server, terradata, etc. as its data warehouse target. Many organisations prefer Informatica to do that ETL process, because Informatica is more powerful in designing and building data warehouses. It can connect to several sources and targets to extract meta data from sources and targets, transform and load the data into target systems.
Consider a Bank that has got many branches throughout the world. In each branch data may be stored in different source systems like oracle, sql server, terradata, etc. When the Bank decides to integrate its data from several sources for its management decisions, it may choose one or more systems like oracle, sql server, terradata, etc. as its data warehouse target. Many organisations prefer Informatica to do that ETL process, because Informatica is more powerful in designing and building data warehouses. It can connect to several sources and targets to extract meta data from sources and targets, transform and load the data into target systems.
Guidelines to work with Informatica Power
Center
- Repository: This is where all the metadata information is stored in the Informatica suite. The Power Center Client and the Repository Server would access this repository to retrieve, store and manage metadata.
- Power Center Client: Informatica client is used for managing users, identifiying source and target systems definitions, creating mapping and mapplets, creating sessions and run workflows etc.
- Repository Server: This repository server takes care of all the connections between the repository and the Power Center Client.
- Power Center Server: Power Center server does the extraction from source and then loading data into targets.
- Designer:
Source Analyzer, Mapping Designer and Warehouse Designer are
tools reside within the Designer wizard. Source Analyzer is used for
extracting metadata from source systems.
Mapping Designer is used to create mapping between sources and targets. Mapping is a pictorial representation about the flow of data from source to target.
Warehouse Designer is used for extracting metadata from target systems or metadata can be created in the Designer itself. - Data Cleansing: The PowerCenter's data cleansing technology improves data quality by validating, correctly naming and standardization of address data. A person's address may not be same in all source systems because of typos and postal code, city name may not match with address. These errors can be corrected by using data cleansing process and standardized data can be loaded in target systems (data warehouse).
- Transformation: Transformations help to transform the source data according to the requirements of target system. Sorting, Filtering, Aggregation, Joining are some of the examples of transformation. Transformations ensure the quality of the data being loaded into target and this is done during the mapping process from source to target.
- Workflow Manager: Workflow helps to load the data from source to target in a sequential manner. For example, if the fact tables are loaded before the lookup tables, then the target system will pop up an error message since the fact table is violating the foreign key validation. To avoid this, workflows can be created to ensure the correct flow of data from source to target.
- Workflow Monitor: This monitor is helpful in monitoring and tracking the workflows created in each Power Center Server.
- Power Center Connect: This component helps to extract data and metadata from ERP systems like IBM's MQSeries, Peoplesoft, SAP, Siebel etc. and other third party applications.
- Power Center Exchange: This component helps to extract data and metadata from ERP systems like IBM's MQSeries, Peoplesoft, SAP, Siebel etc. and other third party applications.
Informatica
 |
| Informatica Tutorial |
Power Exchange:
Informatica Power Exchange as a stand alone service or along with Power Center, helps organizations leverage data by avoiding manual coding of data extraction programs. Power Exchange supports batch, real time and changed data capture options in main frame(DB2, VSAM, IMS etc.,), mid range (AS400 DB2 etc.,), and for relational databases (oracle, sql server, db2 etc) and flat files in unix, linux and windows systems.
Informatica Power Exchange as a stand alone service or along with Power Center, helps organizations leverage data by avoiding manual coding of data extraction programs. Power Exchange supports batch, real time and changed data capture options in main frame(DB2, VSAM, IMS etc.,), mid range (AS400 DB2 etc.,), and for relational databases (oracle, sql server, db2 etc) and flat files in unix, linux and windows systems.
Power Channel:
This helps to transfer large amount of encrypted and compressed data over LAN, WAN, through Firewalls, tranfer files over FTP, etc.
This helps to transfer large amount of encrypted and compressed data over LAN, WAN, through Firewalls, tranfer files over FTP, etc.
Meta Data Exchange:
Metadata Exchange enables organizations to take advantage of the time and effort already invested in defining data structures within their IT environment when used with Power Center. For example, an organization may be using data modeling tools, such as Erwin, Embarcadero, Oracle designer, Sybase Power Designer etc for developing data models. Functional and technical team should have spent much time and effort in creating the data model's data structures(tables, columns, data types, procedures, functions, triggers etc). By using meta deta exchange, these data structures can be imported into power center to identifiy source and target mappings which leverages time and effort. There is no need for informatica developer to create these data structures once again.
Metadata Exchange enables organizations to take advantage of the time and effort already invested in defining data structures within their IT environment when used with Power Center. For example, an organization may be using data modeling tools, such as Erwin, Embarcadero, Oracle designer, Sybase Power Designer etc for developing data models. Functional and technical team should have spent much time and effort in creating the data model's data structures(tables, columns, data types, procedures, functions, triggers etc). By using meta deta exchange, these data structures can be imported into power center to identifiy source and target mappings which leverages time and effort. There is no need for informatica developer to create these data structures once again.
Power Analyzer:
Power Analyzer provides organizations with reporting facilities. PowerAnalyzer makes accessing, analyzing, and sharing enterprise data simple and easily available to decision makers. PowerAnalyzer enables to gain insight into business processes and develop business intelligence.
Power Analyzer provides organizations with reporting facilities. PowerAnalyzer makes accessing, analyzing, and sharing enterprise data simple and easily available to decision makers. PowerAnalyzer enables to gain insight into business processes and develop business intelligence.
With PowerAnalyzer, an organization can extract, filter, format, and analyze
corporate information from data stored in a data warehouse, data mart,
operational data store, or otherdata storage models. PowerAnalyzer is best with
a dimensional data warehouse in a relational database. It can also run reports
on data in any table in a relational database that do not conform to the
dimensional model.
Super Glue:
Superglue is used for loading metadata in a centralized place from several sources. Reports can be run against this superglue to analyze meta data.
Superglue is used for loading metadata in a centralized place from several sources. Reports can be run against this superglue to analyze meta data.
Power Mart:
Power Mart is a departmental version of Informatica for building, deploying, and managing data warehouses and data marts. Power center is used for corporate enterprise data warehouse and power mart is used for departmental data warehouses like data marts. Power Center supports global repositories and networked repositories and it can be connected to several sources. Power Mart supports single repository and it can be connected to fewer sources when compared to Power Center. Power Mart can extensibily grow to an enterprise implementation and it is easy for developer productivity through a codeless environment.
Power Mart is a departmental version of Informatica for building, deploying, and managing data warehouses and data marts. Power center is used for corporate enterprise data warehouse and power mart is used for departmental data warehouses like data marts. Power Center supports global repositories and networked repositories and it can be connected to several sources. Power Mart supports single repository and it can be connected to fewer sources when compared to Power Center. Power Mart can extensibily grow to an enterprise implementation and it is easy for developer productivity through a codeless environment.
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.
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.
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.
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.
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.
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
- Advanced External Procedure Transformation
- External 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.
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.
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.
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.
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.
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 atleast one matching port. 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:
- Normal
- Master Outer
- Detail Outer
- 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.
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.
Difference between Connected and UnConnected
Lookup Transformation:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Sorter Transformation
Sorter transformation is a Connected and an Active transformation. It allows to sort 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 to sort 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.
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.
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.
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.
Advanced External Procedure Transformation
Advanced External Procedure transformation is an Active and Connected transformation. It operates in conjunction with procedures, which are created outside of the Designer interface to extend PowerCenter/PowerMart functionality. It is useful in creating external transformation applications, such as sorting and aggregation, which require all input rows to be processed before emitting any output rows.
Advanced External Procedure transformation is an Active and Connected transformation. It operates in conjunction with procedures, which are created outside of the Designer interface to extend PowerCenter/PowerMart functionality. It is useful in creating external transformation applications, such as sorting and aggregation, which require all input rows to be processed before emitting any output rows.
External Procedure Transformation
External Procedure transformation is an Active and Connected/UnConnected transformations. Sometimes, the standard transformations such as Expression transformation may not provide the functionality that you want. In such cases External procedure is useful to develop complex functions within a dynamic link library (DLL) or UNIX shared library, instead of creating the necessary Expression transformations in a mapping.
External Procedure transformation is an Active and Connected/UnConnected transformations. Sometimes, the standard transformations such as Expression transformation may not provide the functionality that you want. In such cases External procedure is useful to develop complex functions within a dynamic link library (DLL) or UNIX shared library, instead of creating the necessary Expression transformations in a mapping.
Differences between Advanced External
Procedure and External Procedure Transformations:
External Procedure returns single value,
where as Advanced External Procedure returns multiple values.
External Procedure supports COM and
Informatica procedures where as AEP supports only Informatica Procedures.
External Procedure returns single value,
where as Advanced External Procedure returns multiple values.
External Procedure supports COM and
Informatica procedures where as AEP supports only Informatica Procedures.
Good Post! Thank you so much for sharing this pretty post, it was so good to read and useful to improve my knowledge as updated one, keep blogging…
ReplyDeleteRegards,
SAS Training in Chennai|SAS Course in Chennai|SAS Training Institutes in Chennai