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Monday, 16 July 2012

TRANSFORMATIONS PART-2 IN INFORMATICA

SQL TRANSFORMATION :

You can pass the database connection information to the SQL transformation as input data at run time. The transformation processes external SQL scripts or SQL queries that you create in an SQL editor. The SQL transformation processes the query and returns rows and database errors.
When you create an SQL transformation, you configure the following options:
Mode:-The SQL transformation runs in one of the following modes:
  • Script mode. The SQL transformation runs ANSI SQL scripts that are externally located. You pass a script name to the transformation with each input row. The SQL transformation outputs one row for each input row.
  • Query mode. The SQL transformation executes a query that you define in a query editor. You can pass strings or parameters to the query to define dynamic queries or change the selection parameters. You can output multiple rows when the query has a SELECT statement.
  • Passive or active transformation. The SQL transformation is an active transformation by default. You can configure it as a passive transformation when you create the transformation.
  • Database type. The type of database the SQL transformation connects to.
  • Connection type. Pass database connection information to the SQL transformation or use a connection object.
Script Mode
An SQL transformation running in script mode runs SQL scripts from text files. You pass each script file name from the source to the SQL transformation Script Name port. The script file name contains the complete path to the script file.
When you configure the transformation to run in script mode, you create a passive transformation. The transformation returns one row for each input row. The output row contains results of the query and any database error.
Rules and Guidelines for Script Mode
Use the following rules and guidelines for an SQL transformation that runs in script mode:
  • You can use a static or dynamic database connection with script mode.
  • To include multiple query statements in a script, you can separate them with a semicolon.
  • You can use mapping variables or parameters in the script file name.
  • The script code page defaults to the locale of the operating system. You can change the locale of the script.
  • The script file must be accessible by the Integration Service. The Integration Service must have read permissions on the directory that contains the script.
  • The Integration Service ignores the output of any SELECT statement you include in the SQL script. The SQL transformation in script mode does not output more than one row of data for each input row.
  • You cannot use scripting languages such as Oracle PL/SQL or Microsoft/Sybase T-SQL in the script.
  • You cannot use nested scripts where the SQL script calls another SQL script.
  • A script cannot accept run-time arguments.
Query Mode
  • When you configure the SQL transformation to run in query mode, you create an active transformation.
  • When an SQL transformation runs in query mode, it executes an SQL query that you define in the transformation.
  • You pass strings or parameters to the query from the transformation input ports to change the query statement or the query data.
You can create the following types of SQL queries in the SQL transformation:
  • Static SQL query. The query statement does not change, but you can use query parameters to change the data. The Integration Service prepares the query once and runs the query for all input rows.
  •  Dynamic SQL query. You can change the query statements and the data. The Integration Service prepares a query for each input row.
Rules and Guidelines for Query Mode
Use the following rules and guidelines when you configure the SQL transformation to run in query mode:
  • The number and the order of the output ports must match the number and order of the fields in the query SELECT clause.
  • The native data type of an output port in the transformation must match the data type of the corresponding column in the database. The Integration Service generates a row error when the data types do not match.
  • When the SQL query contains an INSERT, UPDATE, or DELETE clause, the transformation returns data to the SQL Error port, the pass-through ports, and the Num Rows Affected port when it is enabled. If you add output ports the ports receive NULL data values.
  • When the SQL query contains a SELECT statement and the transformation has a pass-through port, the transformation returns data to the pass-through port whether or not the query returns database data. The SQL transformation returns a row with NULL data in the output ports.
  • You cannot add the "_output" suffix to output port names that you create.
  • You cannot use the pass-through port to return data from a SELECT query.
  • When the number of output ports is more than the number of columns in the SELECT clause, the extra ports receive a NULL value.
  • When the number of output ports is less than the number of columns in the SELECT clause, the Integration Service generates a row error.
  • You can use string substitution instead of parameter binding in a query. However, the input ports must be string data types.
SQL Transformation Properties
After you create the SQL transformation, you can define ports and set attributes in the following transformation tabs:
  •  Ports. Displays the transformation ports and attributes that you create on the SQL Ports tab.
  •  Properties. SQL transformation general properties.
  •  SQL Settings. Attributes unique to the SQL transformation.
  •  SQL Ports. SQL transformation ports and attributes.
Note: You cannot update the columns on the Ports tab. When you define ports on the SQL Ports tab, they display on the Ports tab.
Properties Tab
Configure the SQL transformation general properties on the Properties tab. Some transformation properties do not apply to the SQL transformation or are not configurable.
The following table describes the SQL transformation properties:

Property Description
Run Time Location Enter a path relative to the Integration Service node that runs the SQL transformation session.
If this property is blank, the Integration Service uses the environment variable defined on the Integration Service node to locate the DLL or shared library.
You must copy all DLLs or shared libraries to the run-time location or to the environment variable defined on the Integration Service node. The Integration Service fails to load the procedure when it cannot locate the DLL, shared library, or a referenced file.
Tracing Level Sets the amount of detail included in the session log when you run a session containing this transformation. When you configure the SQL transformation tracing level to Verbose Data, the Integration Service writes each SQL query it prepares to the session log.
Is Partition able Multiple partitions in a pipeline can use this transformation. Use the following options:
- No. The transformation cannot be partitioned. The transformation and other transformations in the same pipeline are limited to one partition. You might choose No if the transformation processes all the input data together, such as data cleansing.
- Locally. The transformation can be partitioned, but the Integration Service must run all partitions in the pipeline on the same node. Choose Locally when different partitions of the transformation must share objects in memory.
- Across Grid. The transformation can be partitioned, and the Integration Service can distribute each partition to different nodes.
Default is No.
Update Strategy The transformation defines the update strategy for output rows. You can enable this property for query mode SQL transformations.
Default is disabled.
Transformation Scope The method in which the Integration Service applies the transformation logic to incoming data. Use the following options:
- Row
- Transaction
- All Input
Set transaction scope to transaction when you use transaction control in static query mode.
Default is Row for script mode transformations.Default is All Input for query mode transformations.
Output is Repeatable Indicates if the order of the output data is consistent between session runs.
- Never. The order of the output data is inconsistent between session runs.
- Based On Input Order. The output order is consistent between session runs when the input data order is consistent between session runs.
- Always. The order of the output data is consistent between session runs even if the order of the input data is inconsistent between session runs.
Default is Never.
Generate Transaction The transformation generates transaction rows. Enable this property for query mode SQL transformations that commit data in an SQL query.
Default is disabled.
Requires Single
Thread Per Partition
Indicates if the Integration Service processes each partition of a procedure with one thread.
Output is Deterministic The transformation generate consistent output data between session runs. Enable this property to perform recovery on sessions that use this transformation.
Default is enabled.

Create Mapping :
Step 1: Creating a flat file and importing the source from the flat file.
  • Create a Notepad and in it create a table by name bikes with three columns and three records in it.
  • Create one more notepad and name it as path for the bikes. Inside the Notepad just type in (C:\bikes.txt) and save it.
  • Import the source (second notepad) using the source->import from the file. After which we are goanna get a wizard with three subsequent windows and follow the on screen instructions to complete the process of importing the source.
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Step 2: Importing the target and applying the transformation.
In the same way as specified above go to the targets->import from file and select an empty notepad under the name targetforbikes (this is one more blank notepad which we should create and save under the above specified name in the C :\).
  • Create two columns in the target table under the name report and error.
  • We are all set here. Now apply the SQL transformation.
  • In the first window when you apply the SQL transformation we should select the script mode.
  • Connect the SQ to the ScriptName under inputs and connect the other two fields to the output correspondingly.
Snapshot for the above discussed things is given below.
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Step 3: Design the work flow and run it.
  • Create the task and the work flow using the naming conventions.
  • Go to the mappings tab and click on the Source on the left hand pane to specify the path for the output file.
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Step 4: Preview the output data on the target table.

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 NORMALIZER TRANSFORMATION:
  • Active and Connected Transformation.
  • The Normalizer transformation normalizes records from COBOL and relational sources, allowing us to organize the data.
  • Use a Normalizer transformation instead of the Source Qualifier transformation when we normalize a COBOL source.
  • We can also use the Normalizer transformation with relational sources to create multiple rows from a single row of data.
Example 1: To create 4 records of every employee in EMP table.
  • EMP will be source table.
  • Create target table Normalizer_Multiple_Records. Structure same as EMP and datatype of HIREDATE as VARCHAR2.
  • Create shortcuts as necessary.
Creating Mapping :
  1. Open folder where we want to create the mapping.
  2. Click Tools -> Mapping Designer.
  3. Click Mapping-> Create-> Give name. Ex: m_ Normalizer_Multiple_Records
  4. Drag EMP and Target table.
  5. Transformation->Create->Select Expression-> Give name, Click create, done.
  6. Pass all ports from SQ_EMP to Expression transformation.
  7. Transformation-> Create-> Select Normalizer-> Give name, create & done.
  8. Try dragging ports from Expression to Normalizer. Not Possible.
  9. Edit Normalizer and Normalizer Tab. Add columns. Columns equal to columns in EMP table and datatype also same.
  10. Normalizer doesn’t have DATETIME datatype. So convert HIREDATE to char in expression t/f. Create output port out_hdate and do the conversion.
  11. Connect ports from Expression to Normalizer.
  12. Edit Normalizer and Normalizer Tab. As EMPNO identifies source records and we want 4 records of every employee, give OCCUR for EMPNO as 4.
  13. clip_image002
  14. Click Apply and then OK.
  15. Add link as shown in mapping below:
  16. Mapping -> Validate
  17. Repository -> Save
  • Make session and workflow.
  • Give connection information for source and target table.
  • Run workflow and see result.
Example 2: To break rows into columns
Source:
Roll_Number Name ENG HINDI MATHS
100 Amit 78 76 90
101 Rahul 76 78 87
102 Jessie 65 98 79


Target :
Roll_Number Name Marks
100 Amit 78
100 Amit 76
100 Amit 90
101 Rahul 76
101 Rahul 78
101 Rahul 87
102 Jessie 65
102 Jessie 98
102 Jessie 79

  • Make source as a flat file. Import it and create target table.
  • Create Mapping as before. In Normalizer tab, create only 3 ports Roll_Number, Name and Marks as there are 3 columns in target table.
  • Also as we have 3 marks in source, give Occurs as 3 for Marks in Normalizer tab.
  • Connect accordingly and connect to target.
  • Validate and Save
  • Make Session and workflow and Run it. Give Source File Directory and Source File name for source flat file in source properties in mapping tab of session.
  • See the result.
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 SEQUENCE GENERATOR TRANSFORMATION:
  • Passive and Connected Transformation.
  • The Sequence Generator transformation generates numeric values.
  • Use the Sequence Generator to create unique primary key values, replace missing primary keys, or cycle through a sequential range of numbers.
We use it to generate Surrogate Key in DWH environment mostly. When we want to Maintain history, then we need a key other than Primary Key to uniquely identify the record. So we create a Sequence 1,2,3,4 and so on. We use this sequence as the key. Example: If EMPNO is the key, we can keep only one record in target and can’t maintain history. So we use Surrogate key as Primary key and not EMPNO.
Sequence Generator Ports :
The Sequence Generator transformation provides two output ports: NEXTVAL and CURRVAL.
  • We cannot edit or delete these ports.
  • Likewise, we cannot add ports to the transformation.
NEXTVAL:
Use the NEXTVAL port to generate sequence numbers by connecting it to a Transformation or target.
For example, we might connect NEXTVAL to two target tables in a mapping to generate unique primary key values.
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Sequence in Table 1 will be generated first. When table 1 has been loaded, only then Sequence for table 2 will be generated.
CURRVAL:
CURRVAL is NEXTVAL plus the Increment By value.
  • We typically only connect the CURRVAL port when the NEXTVAL port is Already connected to a downstream transformation.
  • If we connect the CURRVAL port without connecting the NEXTVAL port, the Integration Service passes a constant value for each row.
  • when we connect the CURRVAL port in a Sequence Generator Transformation, the Integration Service processes one row in each block.
  • We can optimize performance by connecting only the NEXTVAL port in a Mapping.
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Example: To use Sequence Generator transformation
  • EMP will be source.
  • Create a target EMP_SEQ_GEN_EXAMPLE in shared folder. Structure same as EMP. Add two more ports NEXT_VALUE and CURR_VALUE to the target table.
  • Create shortcuts as needed.
Creating Mapping:
1. Open folder where we want to create the mapping.
2. Click Tools -> Mapping Designer.
3. Click Mapping-> Create-> Give name. Ex: m_seq_gen_example
4. Drag EMP and Target table.
5. Connect all ports from SQ_EMP to target table.
6. Transformation -> Create -> Select Sequence Generator for list -> Create -> Done
7. Connect NEXT_VAL and CURR_VAL from Sequence Generator to target.
8. Validate Mapping
9. Repository -> Save
  • Create Session and then workflow.
  • Give connection information for all tables.
  • Run workflow and see the result in table.
Sequence Generator Properties:
Setting Required/Optional Description
Start Value Required Start value of the generated sequence that we want IS to use if we use Cycle option. Default is 0.
Increment By Required Difference between two consecutive values from the NEXTVAL port.
End Value Optional Maximum value the Integration Service generates.
Current Value Optional First value in the sequence.If cycle option used, the value must be greater than or equal to the start value and less the end value.
Cycle Optional If selected, the Integration Service cycles through the sequence range. Ex: Start Value:1 End Value 10 Sequence will be from 1-10 and again start from 1.
Reset Optional By default, last value of sequence during session is saved to repository. Next time the sequence is started from the valued saved.
If selected, the Integration Service generates values based on the original current value for each session.




Points to Ponder:
  • If Current value is 1 and end value 10, no cycle option. There are 17 records in source. In this case session will fail.
  • If we connect just CURR_VAL only, the value will be same for all records.
  • If Current value is 1 and end value 10, cycle option there. Start value is 0.
  • There are 17 records in source. Sequence: 1 2 – 10. 0 1 2 3 –
  • To make above sequence as 1-10 1-20, give Start Value as 1. Start value is used along with Cycle option only.
  • If Current value is 1 and end value 10, cycle option there. Start value is 1.
  • There are 17 records in source. Session runs. 1-10 1-7. 7 will be saved in repository. If we run session again, sequence will start from 8.
  • Use reset option if you want to start sequence from CURR_VAL every time.
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AGGREGATOR TRANSFORMATION:
  • Connected and Active Transformation
  • The Aggregator transformation allows us to perform aggregate calculations, such as averages and sums.
  • Aggregator transformation allows us to perform calculations on groups.
Components of the Aggregator Transformation
  1. Aggregate expression
  2. Group by port
  3. Sorted Input
  4. Aggregate cache
1) Aggregate Expressions
  • Entered in an output port.
  • Can include non-aggregate expressions and conditional clauses.
The transformation language includes the following aggregate functions:
  • AVG, COUNT, MAX, MIN, SUM
  • FIRST, LAST
  • MEDIAN, PERCENTILE, STDDEV, VARIANCE
Single Level Aggregate Function: MAX(SAL)
Nested Aggregate Function: MAX( COUNT( ITEM ))
Nested Aggregate Functions
  • In Aggregator transformation, there can be multiple single level functions or multiple nested functions.
  • An Aggregator transformation cannot have both types of functions together.
  • MAX( COUNT( ITEM )) is correct.
  • MIN(MAX( COUNT( ITEM ))) is not correct. It can also include one aggregate function nested within another aggregate function
Conditional Clauses
We can use conditional clauses in the aggregate expression to reduce the number of rows used in the aggregation. The conditional clause can be any clause that evaluates to TRUE or FALSE.
  • SUM( COMMISSION, COMMISSION > QUOTA )
Non-Aggregate Functions
We can also use non-aggregate functions in the aggregate expression.
  • IIF( MAX( QUANTITY ) > 0, MAX( QUANTITY ), 0))
2) Group By Ports
  • Indicates how to create groups.
  • When grouping data, the Aggregator transformation outputs the last row of each group unless otherwise specified.
The Aggregator transformation allows us to define groups for aggregations, rather than performing the aggregation across all input data.
For example, we can find Maximum Salary for every Department.
  • In Aggregator Transformation, Open Ports tab and select Group By as needed.
3) Using Sorted Input
  • Use to improve session performance.
  • To use sorted input, we must pass data to the Aggregator transformation sorted by group by port, in ascending or descending order.
  • When we use this option, we tell Aggregator that data coming to it is already sorted.
  • We check the Sorted Input Option in Properties Tab of the transformation.
  • If the option is checked but we are not passing sorted data to the transformation, then the session fails.
4) Aggregator Caches
  • The Power Center Server stores data in the aggregate cache until it completes Aggregate calculations.
  • It stores group values in an index cache and row data in the data cache. If the Power Center Server requires more space, it stores overflow values in cache files.
Note: The Power Center Server uses memory to process an Aggregator transformation with sorted ports. It does not use cache memory. We do not need to configure cache memory for Aggregator transformations that use sorted ports.
1) Aggregator Index Cache:
The index cache holds group information from the group by ports. If we are using Group By on DEPTNO, then this cache stores values 10, 20, 30 etc.
  • All Group By Columns are in AGGREGATOR INDEX CACHE. Ex. DEPTNO
2) Aggregator Data Cache:
DATA CACHE is generally larger than the AGGREGATOR INDEX CACHE.
Columns in Data Cache:
  • Variable ports if any
  • Non group by input/output ports.
  • Non group by input ports used in non-aggregate output expression.
  • Port containing aggregate function

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1) Example: To calculate MAX, MIN, AVG and SUM of salary of EMP table.
  • EMP will be source table.
  • Create a target table EMP_AGG_EXAMPLE in target designer. Table should contain DEPTNO, MAX_SAL, MIN_SAL, AVG_SAL and SUM_SAL
  • Create the shortcuts in your folder.
Creating Mapping:
1. Open folder where we want to create the mapping.
2. Click Tools -> Mapping Designer.
3. Click Mapping-> Create-> Give mapping name. Ex: m_agg_example
4. Drag EMP from source in mapping.
5. Click Transformation -> Create -> Select AGGREGATOR from list. Give name and click Create. Now click done.
6. Pass SAL and DEPTNO only from SQ_EMP to AGGREGATOR Transformation.
7. Edit AGGREGATOR Transformation. Go to Ports Tab
8. Create 4 output ports: OUT_MAX_SAL, OUT_MIN_SAL, OUT_AVG_SAL,
OUT_SUM_SAL
9. Open Expression Editor one by one for all output ports and give the
calculations. Ex: MAX(SAL), MIN(SAL), AVG(SAL),SUM(SAL)
10. Click Apply -> Ok.
11. Drag target table now.
12. Connect the output ports from Rank to target table.
13. Click Mapping -> Validate
14. Repository -> Save
  • Create Session and Workflow as described earlier. Run the Workflow and see the data in target table.
  • Make sure to give connection information for all tables.
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UNION TRANSFORMATION:
  • Active and Connected transformation.
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.
Union Transformation Rules and Guidelines
  • we can create multiple input groups, but only one output group.
  • we can connect heterogeneous sources to a Union transformation.
  • all input groups and the output group must have matching ports. The Precision, data type, and scale must be identical across all groups.
  • The Union transformation does not remove duplicate rows. To remove Duplicate rows, we must add another transformation such as a Router or Filter Transformation.
  • we cannot use a Sequence Generator or Update Strategy transformation upstream from a Union transformation.
Union Transformation Components
When we configure a Union transformation, define the following components:
Transformation tab: We can rename the transformation and add a description.
Properties tab: We can specify the tracing level.
Groups tab: We can create and delete input groups. The Designer displays groups we create on the Ports tab.
Group Ports tab: We can create and delete ports for the input groups. The Designer displays ports we create on the Ports tab.
We cannot modify the Ports, Initialization Properties, Metadata Extensions, or Port Attribute Definitions tabs in a Union transformation.
Create input groups on the Groups tab, and create ports on the Group Ports tab. We can create one or more input groups on the Groups tab. The Designer creates one output group by default. We cannot edit or delete the default output group.
Example: to combine data of tables EMP_10, EMP_20 and EMP_REST
  • Import tables EMP_10, EMP_20 and EMP_REST in shared folder in Sources.
  • Create a target table EMP_UNION_EXAMPLE in target designer. Structure should be same EMP table.
  • Create the shortcuts in your folder.
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Creating Mapping:
  1. Open folder where we want to create the mapping.
  2. Click Tools -> Mapping Designer.
  3. Click Mapping-> Create-> Give mapping name. Ex: m_union_example
  4. Drag EMP_10, EMP_20 and EMP_REST from source in mapping.
  5. Click Transformation -> Create -> Select Union from list. Give name and click Create. Now click done.
  6. Pass ports from SQ_EMP_10 to Union Transformation.
  7. Edit Union Transformation. Go to Groups Tab
  8. One group will be already there as we dragged ports from SQ_DEPT_10 to Union Transformation.
  9. As we have 3 source tables, we 3 need 3 input groups. Click add button to add 2 more groups. See Sample Mapping
  10. We can also modify ports in ports tab.
  11. Click Apply -> Ok.
  12. Drag target table now.
  13. Connect the output ports from Union to target table.
  14. Click Mapping -> Validate
  15. Repository -> Save
  • Create Session and Workflow as described earlier. Run the Workflow and see the data in target table.
  • Make sure to give connection information for all 3 source Tables.
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Sample mapping picture

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JOINER TRANSFORMATION:
  • Connected and Active Transformation
  • Used to join source data from two related heterogeneous sources residing in Different locations or file systems. Or, we can join data  from the same source.
  • If we need to join 3 tables, then we need 2 Joiner Transformations.
  • The Joiner transformation joins two sources with at least one matching port. The Joiner transformation uses a condition that matches one or more pairs of Ports between the two sources.
Example: To join EMP and DEPT tables.
  • EMP and DEPT will be source table.
  • Create a target table JOINER_EXAMPLE in target designer. Table should Contain all ports of EMP table plus DNAME and LOC as shown below.
  • Create the shortcuts in your folder.
Creating Mapping:
  1. Open folder where we want to create the mapping.
  2. Click Tools -> Mapping Designer.
  3. Click Mapping-> Create-> Give mapping name. Ex: m_joiner_example
  4. Drag EMP, DEPT, and Target. Create Joiner Transformation. Link as shown below.
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5. Specify the join condition in Condition tab. See steps on next page.
6. Set Master in Ports tab. See steps on next page.
7. Mapping -> Validate
8. Repository -> Save.
  • Create Session and Workflow as described earlier. Run the Work flow and see the data in target table.
  • Make sure to give connection information for all tables.
JOIN CONDITION:
The join condition contains ports from both input sources that must match for the Power Center Server to join two rows.
Example: DEPTNO=DEPTNO1 in above.
  1. Edit Joiner Transformation -> Condition Tab
  2. Add condition
  • We can add as many conditions as needed.
  • Only = operator is allowed.
If we join Char and Varchar data types, the Power Center Server counts any spaces that pad Char values as part of the string. So if you try to join the following:
Char (40) = “abcd” and Varchar (40) = “abcd”
Then the Char value is “abcd” padded with 36 blank spaces, and the Power Center Server does not join the two fields because the Char field contains trailing spaces.
Note: The Joiner transformation does not match null values.
MASTER and DETAIL TABLES
In Joiner, one table is called as MASTER and other as DETAIL.
  • MASTER table is always cached. We can make any table as MASTER.
  • Edit Joiner Transformation -> Ports Tab -> Select M for Master table.
Table with less number of rows should be made MASTER to improve Performance.
Reason:
  • When the Power Center Server processes a Joiner transformation, it reads rows from both sources concurrently and builds the index and data cache based on the master rows. So table with fewer rows will be read fast and cache can be made as table with more rows is still being read.
  • The fewer unique rows in the master, the fewer iterations of the join comparison occur, which speeds the join process.
JOINER TRANSFORMATION PROPERTIES TAB
  •  Case-Sensitive String Comparison: If selected, the Power Center Server uses case-sensitive string comparisons when performing joins on string columns.
  •  Cache Directory: Specifies the directory used to cache master or detail rows and the index to these rows.
  •  Join Type: Specifies the type of join: Normal, Master Outer, Detail Outer, or Full Outer.
 Tracing Level
 Joiner Data Cache Size
 Joiner Index Cache Size
 Sorted Input
JOIN TYPES
In SQL, a join is a relational operator that combines data from multiple tables into a single result set. The Joiner transformation acts in much the same manner, except that tables can originate from different databases or flat files.
Types of Joins:
  • Normal
  • Master Outer
  • Detail Outer
  • Full Outer
Note: A normal or master outer join performs faster than a full outer or detail outer join.
Example: In EMP, we have employees with DEPTNO 10, 20, 30 and 50. In DEPT, we have DEPTNO 10, 20, 30 and 40. DEPT will be MASTER table as it has less rows.
Normal Join:
With a normal join, the Power Center Server discards all rows of data from the master and detail source that do not match, based on the condition.
  • All employees of 10, 20 and 30 will be there as only they are matching.
Master Outer Join:
This join keeps all rows of data from the detail source and the matching rows from the master source. It discards the unmatched rows from the master source.
  • All data of employees of 10, 20 and 30 will be there.
  • There will be employees of DEPTNO 50 and corresponding DNAME and LOC Columns will be NULL.
Detail Outer Join:
This 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.
  • All employees of 10, 20 and 30 will be there.
  • There will be one record for DEPTNO 40 and corresponding data of EMP columns will be NULL.
Full Outer Join:
A full outer join keeps all rows of data from both the master and detail sources.
  • All data of employees of 10, 20 and 30 will be there.
  • There will be employees of DEPTNO 50 and corresponding DNAME and LOC Columns will be NULL.
  • There will be one record for DEPTNO 40 and corresponding data of EMP Columns will be NULL.
USING SORTED INPUT
  • Use to improve session performance.
  • to use sorted input, we must pass data to the Joiner transformation sorted by the ports that are used in Join Condition.
  • We check the Sorted Input Option in Properties Tab of the transformation.
  • If the option is checked but we are not passing sorted data to the Transformation, then the session fails.
  • We can use SORTER to sort data or Source Qualifier in case of Relational tables.
JOINER CACHES
Joiner always caches the MASTER table. We cannot disable caching. It builds Index cache and Data Cache based on MASTER table.
1) Joiner Index Cache:
  • All Columns of MASTER table used in Join condition are in JOINER INDEX CACHE.
· Example: DEPTNO in our mapping.
2) Joiner Data Cache:
  • Master column not in join condition and used for output to other transformation or target table are in Data Cache.
· Example: DNAME and LOC in our mapping example.
Performance Tuning:
  • Perform joins in a database when possible.
  • Join sorted data when possible.
  • For a sorted Joiner transformation, designate as the master source the source with fewer duplicate key values.
  • Joiner can't be used in following conditions:
  1. Either input pipeline contains an Update Strategy transformation.
  2. We connect a Sequence Generator transformation directly before the Joiner transformation.
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Update Straegy TRANSFORMATION:
  • Active and Connected Transformation
Till now, we have only inserted rows in our target tables. What if we want to update, delete or reject rows coming from source based on some condition?
Example: If Address of a CUSTOMER changes, we can update the old address or keep both old and new address. One row is for old and one for new. This way we maintain the historical data.
Update Strategy is used with Lookup Transformation. In DWH, we create a Lookup on target table to determine whether a row already exists or not. Then we insert, update, delete or reject the source record as per business need.
In Power Center, we set the update strategy at two different levels:
  1. Within a session
  2. Within a Mapping
1. Update Strategy within a session:
When we configure a session, we can instruct the IS to either treat all rows in the same way or use instructions coded into the session mapping to flag rows for different database operations.
Session Configuration:
Edit Session -> Properties -> Treat Source Rows as: (Insert, Update, Delete, and Data Driven). Insert is default. Specifying Operations for Individual Target Tables:
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You can set the following update strategy options:
Insert: Select this option to insert a row into a target table.
Delete: Select this option to delete a row from a table.
Update: We have the following options in this situation:
  •  Update as Update. Update each row flagged for update if it exists in the target table.
  •  Update as Insert. Inset each row flagged for update.
  •  Update else Insert. Update the row if it exists. Otherwise, insert it.
Truncate table: Select this option to truncate the target table before loading data.
2. Flagging Rows within a Mapping
Within a mapping, we use the Update Strategy transformation to flag rows for insert, delete, update, or reject.
Operation Constant Numeric Value
INSERT DD_INSERT 0
UPDATE DD_UPDATE 1
DELETE DD_DELETE 2
REJECT DD_REJECT 3
Update Strategy Expressions:
Frequently, the update strategy expression uses the IIF or DECODE function from the transformation language to test each row to see if it meets a particular condition.
IIF( ( ENTRY_DATE > APPLY_DATE), DD_REJECT, DD_UPDATE )
Or
IIF( ( ENTRY_DATE > APPLY_DATE), 3, 2 )
  • The above expression is written in Properties Tab of Update Strategy T/f.
  • DD means DATA DRIVEN
Forwarding Rejected Rows:
We can configure the Update Strategy transformation to either pass rejected rows to the next transformation or drop them.
Steps:
  1. Create Update Strategy Transformation
  2. Pass all ports needed to it.
  3. Set the Expression in Properties Tab.
  4. Connect to other transformations or target.
Performance tuning:
  1. Use Update Strategy transformation as less as possible in the mapping.
  2. Do not use update strategy transformation if we just want to insert into target table, instead use direct mapping, direct filtering etc.
  3. For updating or deleting rows from the target table we can use Update Strategy transformation itself.
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Lookup TRANSFORMATION:
  • Passive Transformation
  • Can be Connected or Unconnected. Dynamic lookup is connected.
  • Use a Lookup transformation in a mapping to look up data in a flat file or a relational table, view, or synonym.
  • We can import a lookup definition from any flat file or relational database to which both the PowerCenter Client and Server can connect.
  • We can use multiple Lookup transformations in a mapping.
The Power Center Server queries the lookup source based on the lookup ports in the transformation. It compares Lookup transformation port values to lookup source column values based on the lookup condition. Pass the result of the lookup to other transformations and a target.
We can use the Lookup transformation to perform following:
  • Get a related value: EMP has DEPTNO but DNAME is not there. We use Lookup to get DNAME from DEPT table based on Lookup Condition.
  • Perform a calculation: We want only those Employees who’s SAL > Average (SAL). We will write Lookup Override query.
  • Update slowly changing dimension tables: Most important use. We can use a Lookup transformation to determine whether rows already exist in the target.
1. LOOKUP TYPES
We can configure the Lookup transformation to perform the following types of lookups:
  • Connected or Unconnected
  • Relational or Flat File
  • Cached or Un cached
Relational Lookup:
When we create a Lookup transformation using a relational table as a lookup source, we can connect to the lookup source using ODBC and import the table definition as the structure for the Lookup transformation.
  • We can override the default SQL statement if we want to add a WHERE clause or query multiple tables.
  • We can use a dynamic lookup cache with relational lookups.
Flat File Lookup:
When we use a flat file for a lookup source, we can use any flat file definition in the repository, or we can import it. When we import a flat file lookup source, the Designer invokes the Flat File Wizard.
Cached or Un cached Lookup:
We can check the option in Properties Tab to Cache to lookup or not. By default, lookup is cached.
Connected 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.

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2 .LOOKUP T/F COMPONENTS
Define the following components when we configure a Lookup transformation in a mapping:
  • Lookup source
  • Ports
  • Properties
  • Condition
1. Lookup Source:
We can use a flat file or a relational table for a lookup source. When we create a Lookup t/f, we can import the lookup source from the following locations:
  • Any relational source or target definition in the repository
  • Any flat file source or target definition in the repository
  • Any table or file that both the Power Center Server and Client machine can connect to The lookup table can be a single table, or we can join multiple tables in the same database using a lookup SQL override in Properties Tab.
2. Ports:
Ports Lookup
Type
Number
Needed
Description
I Connected
Unconnected
Minimum 1 Input port to Lookup. Usually ports used for Join condition are Input ports.
O Connected
Unconnected
Minimum 1 Ports going to another transformation from Lookup.
L Connected
Unconnected
Minimum 1 Lookup port. The Designer automatically Designates each column in the lookup source as a lookup (L) and output port (O).
R Unconnected 1 Only Return port. Use only in unconnected Lookup t/f only.

3. Properties Tab
Options Lookup Type
Description
Lookup SQL Override Relational Overrides the default SQL statement to query the lookup table.
Lookup Table Name Relational Specifies the name of the table from which the transformation looks up and caches values.
Lookup Caching Enabled Flat File, Relational Indicates whether the Power Center Server caches lookup values during the session.
Lookup Policy on Multiple Match Flat File, Relational Determines what happens when the Lookup transformation finds multiple rows that match the lookup condition. Options: Use First Value or Use Last Value or Use Any Value or Report Error
Lookup Condition Flat File, Relational Displays the lookup condition you set in the Condition tab.
Connection Information Relational Specifies the database containing the lookup table.
Source Type Flat File, Relational Lookup is from a database or flat file.
Lookup Cache Directory Name Flat File, Relational Location where cache is build.
Lookup Cache Persistent Flat File, Relational Whether to use Persistent Cache or not.
Dynamic Lookup Cache Flat File, Relational Whether to use Dynamic Cache or not.
Recache From Lookup Source Flat File, Relational To rebuild cache if cache source changes and we are using Persistent Cache.
Insert Else Update Relational Use only with dynamic caching enabled. Applies to rows entering the Lookup transformation with the row type of insert.
Lookup Data Cache Size Flat File, Relational Data Cache Size
Lookup Index Cache Size Flat File, Relational Index Cache Size
Cache File Name Prefix Flat File, Relational Use only with persistent lookup cache. Specifies the file name prefix to use with persistent lookup cache files.


Some other properties for Flat Files are:
  • Date time Format
  • Thousand Separator
  • Decimal Separator
  • Case-Sensitive String Comparison
  • Null Ordering
  • Sorted Input
4: Condition Tab
We enter the Lookup Condition. The Power Center Server uses the lookup condition to test incoming values. We compare transformation input values with values in the lookup source or cache, represented by lookup ports.
  • The data types in a condition must match.
  • When we enter multiple conditions, the Power Center Server evaluates each condition as an AND, not an OR.
  • The Power Center Server matches null values.
  • The input value must meet all conditions for the lookup to return a value.
  • =, >, <, >=, <=, != Operators can be used.
  • Example: IN_DEPTNO = DEPTNO
In_DNAME = 'DELHI'
Tip: If we include more than one lookup condition, place the conditions with an equal sign first to optimize lookup performance.
Note:
1. We can use = operator in case of Dynamic Cache.
2. The Power Center Server fails the session when it encounters multiple keys for a Lookup transformation configured to use a dynamic cache.
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3. Connected Lookup Transformation
Example: To create a connected Lookup Transformation
  • EMP will be source table. DEPT will be LOOKUP table.
  • Create a target table CONN_Lookup_EXAMPLE in target designer. Table should contain all ports of EMP table plus DNAME and LOC as shown below.
  • Create the shortcuts in your folder.
Creating Mapping:
1. Open folder where we want to create the mapping.
2. Click Tools -> Mapping Designer.
3. Click Mapping-> Create-> Give name. Ex: m_CONN_LOOKUP_EXAMPLE
4. Drag EMP and Target table.
5. Connect all fields from SQ_EMP to target except DNAME and LOC.
6. Transformation-> Create -> Select LOOKUP from list. Give name and click
Create.
7. The Following screen is displayed.
8. As DEPT is the Source definition, click Source and then Select DEPT.
9. Click Ok.
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10. Now Pass DEPTNO from SQ_EMP to this Lookup. DEPTNO from SQ_EMP will be named as DEPTNO1. Edit Lookup and rename it to IN_DEPTNO in ports tab.
11. Now go to CONDITION tab and add CONDITION.
DEPTNO = IN_DEPTNO and Click Apply and then OK.
Link the mapping as shown below:
12. We are not passing IN_DEPTNO and DEPTNO to any other transformation from LOOKUP; we can edit the lookup transformation and remove the OUTPUT check from them.
13. Mapping -> Validate
14. Repository -> Save
  • Create Session and Workflow as described earlier. Run the workflow and see the data in target table.
  • Make sure to give connection information for all tables.
  • Make sure to give connection for LOOKUP Table also.
We use Connected Lookup when we need to return more than one column from Lookup table.There is no use of Return Port in Connected Lookup.
SEE PROPERTY TAB FOR ADVANCED SETTINGS
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4. Unconnected Lookup Transformation
An unconnected Lookup transformation is separate from the pipeline in the mapping. We write an expression using the :LKP reference qualifier to call the lookup within another transformation.
Steps to configure Unconnected Lookup:
  1. Add input ports.
  2. Add the lookup condition.
  3. Designate a return value.
  4. Call the lookup from another transformation.
Example: To create a unconnected Lookup Transformation
  • EMP will be source table. DEPT will be LOOKUP table.
  • Create a target table UNCONN_Lookup_EXAMPLE in target designer. Table should contain all ports of EMP table plus DNAME as shown below.
  • Create the shortcuts in your folder.
Creating Mapping:
1. Open folder where we want to create the mapping.
2. Click Tools -> Mapping Designer.
3. Click Mapping-> Create-> Give name. Ex: m_UNCONN_LOOKUP_EXAMPLE
4. Drag EMP and Target table.
5. Now Transformation-> Create -> Select EXPRESSION from list. Give name
and click Create. Then Click Done.
6. Pass all ports from SQ_EMP to EXPRESSION transformation.
7. Connect all fields from EXPRESSION to target except DNAME.
8. Transformation-> Create -> Select LOOKUP from list. Give name and click
Create.
9. Follow the steps as in Connected above to create Lookup on DEPT table.
10. Click Ok.
11. Now Edit the Lookup Transformation. Go to Ports tab.
12. As DEPTNO is common in source and Lookup, create a port IN_DEPTNO
ports tab. Make it Input port only and Give Datatype same as DEPTNO.
13. Designate DNAME as Return Port. Check on R to make it.
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14. Now add a condition in Condition Tab.
DEPTNO = IN_DEPTNO and Click Apply and then OK.
15. Now we need to call this Lookup from Expression Transformation.
16. Edit Expression t/f and create a new output port out_DNAME of data type as DNAME. Open the Expression editor and call Lookup as given below:
We double click Unconn in bottom of Functions tab and as we need only
DEPTNO, we pass only DEPTNO as input.
17. Validate the call in Expression editor and Click OK.
18. Mapping -> Validate
19. Repository Save.
  • Create Session and Workflow as described earlier. Run the workflow and see the data in target table.
  • Make sure to give connection information for all tables.
  • Make sure to give connection for LOOKUP Table also.
5. Lookup Caches
We can configure a Lookup transformation to cache the lookup table. The Integration Service (IS) builds a cache in memory when it processes the first row of data in a cached Lookup transformation.
The Integration Service also creates cache files by default in the $PMCacheDir. If the data does not fit in the memory cache, the IS stores the overflow values in the cache files. When session completes, IS releases cache memory and deletes the cache files.
  • If we use a flat file lookup, the IS always caches the lookup source.
  • We set the Cache type in Lookup Properties.
Lookup Cache Files
1. Lookup Index Cache:
  • Stores data for the columns used in the lookup condition.
2. Lookup Data Cache:
  • For a connected Lookup transformation, stores data for the connected output ports, not including ports used in the lookup condition.
  • For an unconnected Lookup transformation, stores data from the return port.
Types of Lookup Caches:
1. Static Cache
By default, the IS creates a static cache. It caches the lookup file or table and Looks up values in the cache for each row that comes into the transformation.The IS does not update the cache while it processes the Lookup transformation.
2. Dynamic Cache
To cache a target table or flat file source and insert new rows or update existing rows in the cache, use a Lookup transformation with a dynamic cache.
The IS dynamically inserts or updates data in the lookup cache and passes data to the target. Target table is also our lookup table. No good for performance if table is huge.
3. Persistent Cache
If the lookup table does not change between sessions, we can configure the Lookup transformation to use a persistent lookup cache.
The IS saves and reuses cache files from session to session, eliminating the time Required to read the lookup table.
4. Recache from Source
If the persistent cache is not synchronized with the lookup table, we can Configure the Lookup transformation to rebuild the lookup cache.If Lookup table has changed, we can use this to rebuild the lookup cache.
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5. Shared Cache
  •  Unnamed cache: When Lookup transformations in a mapping have compatible caching structures, the IS shares the cache by default. You can only share static unnamed caches.
  •  Named cache: Use a persistent named cache when we want to share a cache file across mappings or share a dynamic and a static cache. The caching structures must match or be compatible with a named cache. You can share static and dynamic named caches.
Building Connected Lookup Caches
We can configure the session to build caches sequentially or concurrently.
  • When we build sequential caches, the IS creates caches as the source rows enter the Lookup transformation.
  • When we configure the session to build concurrent caches, the IS does not wait for the first row to enter the Lookup transformation before it creates caches. Instead, it builds multiple caches concurrently.
1. Building Lookup Caches Sequentially:
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2. Building Lookup Caches Concurrently:
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  • To configure the session to create concurrent caches
Edit Session -> In Config Object Tab-> Additional Concurrent Pipelines for
Lookup Cache Creation -> Give a value here (Auto By Default)
Note: The IS builds caches for unconnected Lookups sequentially only

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Expression TRANSFORMATION:
  • Passive and connected transformation.
Use the Expression transformation to calculate values in a single row before we write to the target. For example, we might need to adjust employee salaries, concatenate first and last names, or convert strings to numbers.
Use the Expression transformation to perform any non-aggregate calculations.
Example: Addition, Subtraction, Multiplication, Division, Concat, Uppercase conversion, lowercase conversion etc.
We can also use the Expression transformation to test conditional statements before we output the results to target tables or other transformations. Example: IF, Then, Decode
There are 3 types of ports in Expression Transformation:
  • Input
  • Output
  • Variable: Used to store any temporary calculation.
Calculating Values :
To use the Expression transformation to calculate values for a single row, we must include the following ports:
  • Input or input/output ports for each value used in the calculation: For example: To calculate Total Salary, we need salary and commission.
  •  Output port for the expression: We enter one expression for each output port. The return value for the output port needs to match the return value of the expression.
We can enter multiple expressions in a single Expression transformation. We can create any number of output ports in the transformation.
Example: Calculating Total Salary of an Employee
  • Import the source table EMP in Shared folder. If it is already there, then don’t  import.
  • In shared folder, create the target table Emp_Total_SAL. Keep all ports as in EMP table except Sal and Comm in target table. Add Total_SAL port to store the calculation.
  • Create the necessary shortcuts in the folder.
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Creating Mapping:
  1. Open folder where we want to create the mapping.
  2. Click Tools -> Mapping Designer.
  3. Click Mapping -> Create -> Give mapping name. Ex: m_totalsal
  4. Drag EMP from source in mapping.
  5. Click Transformation -> Create -> Select Expression from list. Give name and click Create. Now click done.
  6. Link ports from SQ_EMP to Expression Transformation.
  7. Edit Expression Transformation. As we do not want Sal and Comm in target, remove check from output port for both columns.
  8. Now create a new port out_Total_SAL. Make it as output port only.
  9. Click the small button that appears in the Expression section of the dialog box and enter the expression in the Expression Editor.
  10. Enter expression SAL + COMM. You can select SAL and COMM from Ports tab in expression editor.
  11. Check the expression syntax by clicking Validate.
  12. Click OK -> Click Apply -> Click Ok.
  13. Now connect the ports from Expression to target table.
  14. Click Mapping -> Validate
  15. Repository -> Save
Create Session and Workflow as described earlier. Run the workflow and see the data in target table.
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As COMM is null, Total_SAL will be null in most cases. Now open your mapping and expression transformation. Select COMM port, In Default Value give 0. Now apply changes. Validate Mapping and Save.
Refresh the session and validate workflow again. Run the workflow and see the result again.
Now use ERROR in Default value of COMM to skip rows where COMM is null.
Syntax: ERROR(‘Any message here’)
Similarly, we can use ABORT function to abort the session if COMM is null.
Syntax: ABORT(‘Any message here’)
Make sure to double click the session after doing any changes in mapping. It will prompt that mapping has changed. Click OK to refresh the mapping. Run workflow after validating and saving the workflow.
Performance tuning :
Expression transformation is used to perform simple calculations and also to do Source lookups.
  1. Use operators instead of functions.
  2. Minimize the usage of string functions.
  3. If we use a complex expression multiple times in the expression transformer, then Make that expression as a variable. Then we need to use only this variable for all computations.
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Router Transformation :
  • Active and connected 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.
Example: If we want to keep employees of France, India, US in 3 different tables, then we can use 3 Filter transformations or 1 Router transformation.
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Mapping A uses three Filter transformations while Mapping B produces the same result with one Router transformation.
A Router transformation consists of input and output groups, input and output ports, group filter conditions, and properties that we configure in the Designer.
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Working with Groups
A Router transformation has the following types of groups:
  • Input: The Group that gets the input ports.
  • Output: User Defined Groups and Default Group. We cannot modify or delete Output ports or their properties.
User-Defined Groups: We create a user-defined group to test a condition based on incoming data. A user-defined group consists of output ports and a group filter Condition. We can create and edit user-defined groups on the Groups tab with the Designer. Create one user-defined group for each condition that we want to specify.
The Default Group: The Designer creates the default group after we create one new user-defined group. The Designer does not allow us to edit or delete the default group. This group does not have a group filter condition associated with it. If all of the conditions evaluate to FALSE, the IS passes the row to the default group.
Example: Filtering employees of Department 10 to EMP_10, Department 20 to EMP_20 and rest to EMP_REST
  • Source is EMP Table.
  • Create 3 target tables EMP_10, EMP_20 and EMP_REST in shared folder. Structure should be same as EMP table.
  • Create the shortcuts in your folder.
Creating Mapping:
1. Open folder where we want to create the mapping.
2. Click Tools -> Mapping Designer.
3. Click Mapping-> Create-> Give mapping name. Ex: m_router_example
4. Drag EMP from source in mapping.
5. Click Transformation -> Create -> Select Router from list. Give name and
Click Create. Now click done.
6. Pass ports from SQ_EMP to Router Transformation.
7. Edit Router Transformation. Go to Groups Tab
8. Click the Groups tab, and then click the Add button to create a user-defined Group. The default group is created automatically..
9. Click the Group Filter Condition field to open the Expression Editor.
10. Enter a group filter condition. Ex: DEPTNO=10
11. Click Validate to check the syntax of the conditions you entered.
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12. Create another group for EMP_20. Condition: DEPTNO=20
13. The rest of the records not matching the above two conditions will be passed to DEFAULT group. See sample mapping
14. Click OK -> Click Apply -> Click Ok.
15. Now connect the ports from router to target tables.
16. Click Mapping -> Validate
17. Repository -> Save
  • Create Session and Workflow as described earlier. Run the Workflow and see the data in target table.
  • Make sure to give connection information for all 3 target tables.
Sample Mapping:
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Difference between Router and Filter :
We cannot pass rejected data forward in filter but we can pass it in router. Rejected data is in Default Group of router.
 
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