Informatica interview questions with detailed answers

A source qualifier and a filter transformation are both used in Informatica for data transformation, but they serve different purposes.

A source qualifier is used to define the properties of the source data, such as the database connection details, the columns to be used, and the query to extract data.

On the other hand, a filter transformation is used to filter rows based on a condition. It allows only those rows that satisfy the specified condition to pass through to the next transformation.

Here are some examples of how they can be used in Informatica:

Source Qualifier example:

SELECT COLUMN_A, COLUMN_B, COLUMN_C
FROM TABLE_A
WHERE COLUMN_D > 10;

Filter transformation example:

Filter condition: COLUMN_D > 10

In the above examples, the source qualifier is used to extract data from a table and specify the condition for selecting the columns. The filter transformation, on the other hand, is used to filter out rows where COLUMN_D is not greater than 10.

In Informatica, data loading can be handled using the PowerCenter tool, which provides several options to load data from various sources into the target system.

The following are some common ways to handle data loading in Informatica:

• Direct Loading: Data can be loaded directly into the target table using the target table's native API. Here's an example SQL override query:

INSERT INTO TARGET_TABLE (COL1, COL2, COL3)
VALUES (:COL1, :COL2, :COL3)

• Bulk Loading: Data can be loaded in bulk using the target database's bulk loader utility. Here's an example using SQL*Loader:

sqlldr userid=username/password control=example.ctl

• External Loader: Data can be loaded using external tools such as Informatica External Loader (IEL), which loads data into the target database using bulk loading techniques. Here's an example:

iel run -session mysession -config myconfig.xml

These are just a few examples of how data loading can be handled in Informatica, and there are many other options available depending on the specific use case.

In Informatica, a mapping is a set of transformations that define how data is transformed from source to target. A session, on the other hand, is an execution instance of a mapping that runs on the Integration Service.

Here are some examples of how they can be used in Informatica:

Mapping example:

Source --> Expression --> Aggregator --> Target

In the above example, the mapping defines the transformation logic that takes data from a source, applies an expression transformation, aggregates the data, and then loads it into a target.

Session example:

Session Name: MySession
Mapping Name: MyMapping

In the above example, the session is an execution instance of the MyMapping mapping. It defines the specific run-time properties such as database connections, log files, and error handling that are used when the mapping is run on the Integration Service.

In summary, a mapping defines the data transformation logic, while a session defines the execution properties and runtime behavior of the mapping.

In Informatica, a target table is defined in the mapping designer interface, where the target table properties such as table name, column names, and data types are specified.

Here's an example of how to define a target table in Informatica:

1. Open the mapping designer interface and create a new mapping.
2. Drag and drop the target table from the Sources/Targets tab onto the mapping canvas.
3. Double-click the target table to open the target table properties.
4. In the General tab, specify the target table name, database connection details, and any other relevant information.
5. In the Columns tab, define the column names, data types, and any other properties for each column in the target table.
6. Click OK to save the target table definition.

Here's an example of how the target table definition might look in the mapping designer interface:

Target Table: MY_TARGET_TABLE

General Tab:
Database Connection: MY_DATABASE_CONNECTION
Table Name: MY_TARGET_TABLE

Columns Tab:
Column Name | Data Type | Precision | Scale | Other Properties
---------------------------------------------------------------
COLUMN_A   | Integer  |           |       |
COLUMN_B   | String   |    10     |       |
COLUMN_C   | Date     |           |       | Format: MM/DD/YYYY

Once the target table is defined, it can be used as a target in the mapping to transform and load data.

In Informatica, a lookup transformation is used to look up data from a relational table, view, or flat file. The purpose of a lookup transformation is to match data from a source table with data from a reference table, and then use the matched data to perform further transformations or to update the target table.

Here's an example of how a lookup transformation might be used in Informatica:

Source --> Lookup --> Expression --> Target

In the above example, the lookup transformation is used to look up data from a reference table based on a matching key in the source data. The matched data is then used in an expression transformation to perform further transformations before loading the data into the target table.

In Informatica, a joiner transformation is used to join two or more sources based on a common key. The joiner transformation combines rows from two or more source tables into a single row, where the rows are matched based on the common key columns.

Here's an example of how a joiner transformation might be used in Informatica:

Source1 --> Joiner --> Target
           |
Source2 ---|

In the above example, the joiner transformation is used to join two source tables based on a common key column. The matched rows from the two sources are combined into a single row and sent to the target table for further processing. The joiner transformation supports different types of joins such as inner join, outer join, and full outer join, depending on the specific use case.

In Informatica, a reusable transformation can be created by defining a transformation in a separate transformation file, which can be used in multiple mappings. Here's an example of how to create a reusable transformation in Informatica:

1. Open the transformation developer interface and create a new transformation.
2. Define the transformation logic and configure any required inputs and outputs.
3. Save the transformation as a reusable transformation file with a .xml extension.
4. In a mapping, drag and drop the reusable transformation from the Transformation Developer tab onto the mapping canvas.
5. Configure the input and output ports for the reusable transformation to match the mapping requirements.
6. Link the reusable transformation to other transformations in the mapping as needed.

Here's an example of how the reusable transformation definition might look in the transformation developer interface:

Transformation: MY_REUSABLE_TRANSFORM

Input Ports:
Port Name | Data Type | Precision | Scale
------------------------------------------
IN_PORT1  | Integer  |           |
IN_PORT2  | String   |    10     |

Output Ports:
Port Name | Data Type | Precision | Scale
------------------------------------------
OUT_PORT1 | String   |           |
OUT_PORT2 | Date     |           |

Transformation Logic:
OUT_PORT1 = IN_PORT1 || IN_PORT2
OUT_PORT2 = TO_DATE('01/01/2000', 'MM/DD/YYYY')

Once the reusable transformation is created, it can be used in multiple mappings, making it a powerful tool for reusing code and reducing development time.

In Informatica, a router transformation is used to route data rows from a source to different targets based on a set of conditional expressions. The purpose of a router transformation is to split data based on specific criteria and send each split to a different target for further processing.

Here's an example of how a router transformation might be used in Informatica:

Source --> Router --> Target1
                    |
                    --> Target2

In the above example, the router transformation is used to split the source data into two streams based on a set of conditional expressions. Each split is then sent to a different target for further processing. The router transformation can be used to split data based on a variety of criteria, such as range-based filters or string matching.

In Informatica, errors in a session can be identified and fixed using session logs and error messages. Here's an example of how to troubleshoot errors in an Informatica session:

1. Check the session log to identify any error messages or warnings.
2. Review the session properties and configurations to ensure they are correct and match the mapping requirements.
3. Check the connectivity and permissions for the source and target databases.
4. Check for any data type mismatches or transformation errors in the mapping.
5. Use breakpoints and debugging tools to identify issues with the mapping logic.
6. Use the debugger to step through the mapping logic and identify the source of the error.

By following these steps and using Informatica's built-in debugging and troubleshooting tools, it's possible to quickly identify and fix errors in an Informatica session.

In Informatica, a connected lookup transformation is used within a mapping to perform lookups on source data, while an unconnected lookup transformation is used outside of a mapping, typically as a reusable transformation.

Here's an example of the difference between connected and unconnected lookup transformations:

Connected Lookup:

Source --> Mapping --> Connected Lookup --> Target

In this example, the connected lookup transformation is used within a mapping to perform a lookup on source data and return the lookup value to the mapping for further processing.

Unconnected Lookup:

Source --> Unconnected Lookup --> Target

In this example, the unconnected lookup transformation is used outside of a mapping, typically as a reusable transformation, to perform a lookup on source data and return the lookup value to the calling program or script.

The key difference between connected and unconnected lookup transformations is that the former is used within a mapping and is integrated into the data flow, while the latter is used outside of a mapping and typically requires a separate calling program or script to use its functionality.

In Informatica, incremental data loads can be handled using a combination of source qualifiers and filters. Here's an example of how to handle incremental data loads in Informatica:

1. Add a source qualifier to the mapping and set it to read only new or updated records.

SELECT *
FROM source_table
WHERE update_date > $$LAST_EXTRACT_DATE

1. Create a session-level parameter named $$LAST_EXTRACT_DATE and set its value to the date and time of the last successful data load.
2. Use a filter transformation to filter out any records that have already been processed.

update_date > $$LAST_EXTRACT_DATE AND processed_flag = 'N'

1. After the session completes successfully, update the value of $$LAST_EXTRACT_DATE to the date and time of the most recent extraction.

By using source qualifiers, filters, and session-level parameters, it's possible to implement incremental data loads in Informatica and efficiently process only new or updated records.

In Informatica, surrogate keys can be handled using the Sequence Generator transformation. Here's an example of how to handle surrogate keys in Informatica:

1. Add a Sequence Generator transformation to the mapping.
2. Configure the Sequence Generator to generate unique surrogate keys for each new record.

Start value: 1
Increment by: 1
Max value: 99999999
Cycle option: No

1. Connect the output of the Sequence Generator to the target table, mapping the surrogate key to the appropriate field.

By using the Sequence Generator transformation, it's possible to generate unique surrogate keys for each new record and ensure data integrity in the target table.

In Informatica, the Sequence Generator transformation is used to generate unique sequential values, such as surrogate keys, for use in a mapping. The Sequence Generator generates a range of numbers based on a starting value, an increment value, and a maximum value. These generated values can then be used as key values in a target table, or as reference values for lookups in the mapping.

Here's an example of how to use the Sequence Generator transformation in a mapping:

Source --> Expression --> Sequence Generator --> Target

In this example, the Sequence Generator transformation generates unique sequential values for use as surrogate keys in the target table. The generated values are passed through an Expression transformation for any required data manipulation before being written to the target table.

The Aggregator and Sorter transformations are both used for data manipulation in Informatica, but they serve different purposes.

The Aggregator transformation performs calculations on groups of data based on specified groupings and operations, and it is typically used to summarize data. Here's an example:

Source --> Aggregator --> Target

In this example, the Aggregator transformation groups data based on specified columns and performs aggregate functions such as sum, average, or count on those groups before writing the results to the target.

The Sorter transformation, on the other hand, sorts data based on specified columns and order. Here's an example:

Source --> Sorter --> Target

In this example, the Sorter transformation sorts the data based on specified columns and order before writing the sorted results to the target.

In summary, the Aggregator transformation is used to perform calculations on groups of data, while the Sorter transformation is used to sort data based on specified columns and order.

To create a source definition in Informatica, follow these steps:

1. Open the PowerCenter Designer tool and create a new mapping.
2. In the Mapping Designer, select the Sources tab.
3. Right-click in the empty space and select "Import from Database" or "Import from File" depending on your data source.
4. Follow the prompts to specify the connection details and select the table or file from which you want to import the source definition.
5. Preview and optionally edit the source definition.
6. Save the source definition.

Here's an example of importing a source definition from a database:

1. In the Mapping Designer, select the Sources tab.
2. Right-click and select "Import from Database."
3. In the Import from Database wizard, select the database connection and enter the SQL query or select the table from which you want to import the source definition.
4. Preview and optionally edit the source definition, such as changing data types or adding filters.
5. Save the source definition.

After creating the source definition, it can be used in mappings to extract data from the source and perform transformations before loading it into the target.

In Informatica, a flat file source is a type of source definition that represents data stored in a flat file format, such as CSV, tab-delimited, or fixed-width files. It contains information about the format of the file, including the field delimiter, record delimiter, and data types of each column.

To create a flat file source in Informatica, follow these steps:

1. In the Mapping Designer, select the Sources tab.
2. Right-click and select "Import from File".
3. Follow the prompts to specify the location and format of the flat file.
4. Preview and optionally edit the source definition, such as changing data types or specifying filters.
5. Save the source definition.

Here's an example of importing a flat file source definition:

Source Definition Name: CUSTOMERS_FLATFILE
File Name: /path/to/customers.csv
File Format: Delimited
Field Delimiter: ,
Record Delimiter: \n
Column Definitions:
- CustomerID: Integer
- CustomerName: String(50)
- Address: String(100)
- City: String(50)
- State: String(20)
- Zip: String(10)

Once the flat file source definition is created, it can be used in mappings to extract data from the flat file and perform transformations before loading it into the target.

In Informatica, a session log is a record of the activities performed during the execution of a session. It provides details about the sources, targets, transformations, and other components used in the session, as well as any errors or warnings encountered.

To enable session logging in Informatica, you need to configure the session properties to specify the log file name and level of detail to be captured. Here's an example of session log properties:

Log File Directory: /path/to/logs
Log File Name: mysession.log
Log Verbosity: Verbose Initialization

Once session logging is enabled, the log file will be generated after the session execution completes. You can use the log file to troubleshoot errors and monitor the performance of the session.

In Informatica, a workflow is a collection of sessions, commands, and other control elements that define the sequence of tasks to be executed. To create a workflow, follow these steps:

1. Open the Workflow Manager tool and create a new folder to store the workflow.
2. Right-click on the folder and select "New Workflow" from the context menu.
3. In the Workflow Designer window, drag and drop the sessions or commands you want to include in the workflow.
4. Connect the components in the workflow by drawing lines between them to define the sequence of tasks.
5. Configure the workflow properties, such as name, description, and start time.
6. Validate the workflow to check for any errors or warnings.
7. Save and deploy the workflow to the Informatica server.

Here's an example of a simple workflow that consists of two sessions:

Workflow: myworkflow
Description: This workflow loads data from source to target.

Session 1: load_customer_data
Description: Loads data from the customer source to the customer target.
Connection: source_connection, target_connection
Mapping: customer_mapping

Session 2: load_order_data
Description: Loads data from the order source to the order target.
Connection: source_connection, target_connection
Mapping: order_mapping

Start: load_customer_data

This workflow starts with the "load_customer_data" session and then proceeds to the "load_order_data" session once the first session completes successfully.

In Informatica, the Mapping Designer is a graphical tool used to create mappings between source and target objects. It provides an intuitive drag-and-drop interface for creating transformations, defining data flows, and configuring the properties of the objects used in a mapping. The Mapping Designer allows developers to easily build complex mappings with various transformations such as filters, aggregators, lookups, and more.

In Informatica, a repository is a centralized location where all the metadata related to the ETL process is stored. It includes mappings, sessions, workflows, and other objects created in the Informatica PowerCenter. The repository is accessed through the Informatica Client tools, and it ensures consistency and version control across different development environments. The repository also provides security and access controls for the objects stored in it. Here is an example of connecting to a repository using the Repository Manager tool in Informatica:

1. Launch the Repository Manager tool.
2. Click on the Connect icon.
3. Enter the appropriate login credentials for the repository.
4. Select the repository and click on Connect.
5. Once connected, you can access the objects stored in the repository.

To create a target definition in Informatica:

1. Open the Mapping Designer and create a new mapping.
2. Drag the source and target definitions onto the mapping designer workspace.
3. Connect the source and target definitions with a transformation, such as a mapping or filter transformation.
4. Define the target properties, such as target type and target field properties.
5. Save the mapping and create a session to run the mapping and load data into the target.

In Informatica, a full data load loads all the data from the source into the target, replacing any existing data. An incremental data load only loads new or changed data since the last load, and preserves existing data. Incremental loads are typically faster and use less resources than full loads.

Here's an example of an incremental load using a mapping variable:

SETV $LAST_LOAD_DATE = '2022-01-01';

SELECT *
FROM MY_SOURCE_TABLE
WHERE LOAD_DATE > $LAST_LOAD_DATE;

In this example, only the rows with a LOAD_DATE greater than the last load date will be selected for the incremental load. The $LAST_LOAD_DATE mapping variable is used to keep track of the last load date.

Data validation in Informatica can be handled through various ways such as using a router transformation to route data to different targets based on business rules, using a filter transformation to filter out invalid data, or using an aggregator transformation to calculate the expected value and compare it with the incoming data. Additionally, error handling mechanisms such as rejecting or redirecting invalid data can be used. Informatica also provides data validation rules to ensure the quality of data. Below is an example of using a filter transformation to filter out invalid data:

ExampleFilterTransformation:
  Type: Filter
  Condition: IIF(ISNULL(OrderDate) OR ISNULL(OrderID), FALSE, TRUE)

Expression transformation is used to perform non-aggregate calculations or manipulate data during ETL processing. To use an expression transformation in Informatica, follow these steps:

1. Drag the expression transformation from the toolbox to the mapping designer.
2. Connect the expression transformation to the appropriate source or target.
3. Open the expression transformation and create a new expression.
4. Use functions and operators to manipulate the data.
5. Define the output ports and connect them to the next transformation.

Here is an example of an expression transformation that concatenates two columns from a source table:

CONCATENATE_COLUMNS = COLUMN1 || COLUMN2

In Informatica, a transformation is a function that is applied to data as it moves from a source to a target in an ETL (Extract, Transform, Load) process. Transformations can be used to manipulate data, apply business rules, filter data, aggregate data, and perform other operations. Examples of transformations in Informatica include the Expression Transformation, Aggregator Transformation, Lookup Transformation, and Joiner Transformation. Transformations can be connected in a mapping to define the flow of data from source to target.

In Informatica, a data mart is a subset of a larger data warehouse that is designed to serve a specific business function or department. It contains a focused set of data that is relevant to the needs of a particular group of users. Data marts can be created using Informatica PowerCenter, which can extract, transform, and load data from various sources and consolidate it into a smaller, more targeted data set. By using data marts, organizations can improve data accessibility, reduce data redundancy, and enable better decision-making by providing more relevant data to the right people.

The Repository Manager in Informatica is a tool used to administer and maintain the PowerCenter repository. It allows users to create and manage repository objects, such as folders, mappings, sessions, and workflows. With Repository Manager, users can also grant permissions to different users, configure repository connections, and back up or restore repository data. Here is an example of creating a new folder in Repository Manager using the PowerCenter Designer:

1. Open Repository Manager and log in.
2. Right-click on the repository where you want to create the folder and select "New Folder".
3. Enter a name for the folder and a description if desired.
4. Click "OK" to create the new folder.

A Union transformation in Informatica is used to combine data from two or more sources that have the same structure. The transformation merges data from different pipelines and outputs a single pipeline. It has the option to remove duplicates based on specific columns. A Union transformation is useful when there is a requirement to combine data from multiple sources, such as combining sales data from different regions. Here is an example of how to use the Union transformation:

SELECT name, age, address FROM source1
UNION
SELECT name, age, address FROM source2;

This will combine the data from source1 and source2 based on the same columns, removing duplicates if necessary, and output a single pipeline.

To create a session in Informatica, follow these steps:

1. Open Informatica PowerCenter Designer and create a new session task under a workflow.
2. Configure the session properties such as source and target connections, mapping, and session options.
3. Set the mapping variables, if necessary, to provide dynamic values at run time.
4. Validate and save the session.
5. Create a workflow to run the session task.
6. Configure the workflow properties and session task properties such as database connections and log files.
7. Save and validate the workflow.

Here's an example of how to create a session using the pmcmd command line tool:

pmcmd startworkflow -sv <Integration Service> -d <Domain Name> -u <User Name> -p <Password> -f <Folder Name> -w <Workflow Name>

In Informatica, data quality issues can be handled by using transformations such as the Expression, Filter, and Lookup transformations, as well as by implementing business rules within the mappings. Additionally, Informatica provides tools such as the Data Quality and Address Doctor transformations to address data quality concerns. The following example shows how to use a Filter transformation to remove invalid data from a source table:

Filter_Transformation = IIF(ISNULL([Column1]),FALSE,TRUE)

This expression checks whether Column1 is null and returns a value of FALSE if it is, or TRUE if it is not null. The Filter transformation can then be used to remove all rows with a FALSE value, effectively filtering out any invalid data.

To create a parameter file in Informatica, you can follow these steps:

1. Open a text editor such as Notepad.
2. Define the parameters in the following format: $Param_name=Param_value
3. Save the file with a .txt extension.
4. In the Informatica Workflow Manager, right-click on the workflow and select "Edit".
5. In the workflow properties, select the "Parameter Files" tab.
6. Click on "Add" and browse to select the parameter file.
7. Click on "OK" to save the changes.

Example parameter file:

$SourceFile=/path/to/source/file.csv
$TargetFile=/path/to/target/file.csv
$Date=2022-02-28

The purpose of a dynamic lookup transformation in Informatica is to perform a lookup on a table at run time, rather than at design time. This allows for more flexibility and adaptability in the mapping, as the lookup table can be changed without having to modify the mapping. Dynamic lookups use an SQL override to perform the lookup based on a parameter or variable value passed in at runtime. Here is an example of a dynamic lookup SQL override:

SELECT employee_name, department_name, salary
FROM employee_table e, department_table d
WHERE e.department_id = d.department_id
AND employee_id = ?

Slowly changing dimensions (SCD) are handled using the SCD type in the mapping designer. Type 1 only retains the most recent record and overwrites old data, type 2 retains all changes by creating a new record, and type 3 retains some old data by adding columns for historical changes. In Informatica, SCDs can be handled using the SCD Type 1, Type 2, or Type 3 transformation. For example, the following code shows the use of an SCD Type 2 transformation to capture historical changes:

INSERT INTO employee_history
SELECT emp_id, emp_name, hire_date, term_date, 'UPDATE' as action, GETDATE() as date_modified
FROM employee
WHERE emp_id = ?

Here, the Type 2 transformation inserts a new record into the history table every time a change is made to the employee table, allowing for historical tracking of changes.

Command tasks in Informatica are used to execute shell commands or scripts. To use a command task, you can follow these steps:

1. Create a command task in the workflow manager.
2. Enter the shell command or script to be executed in the command task editor.
3. Map the command task to a workflow.

Here's an example of how to use a command task to execute a shell command:

1. In the workflow manager, create a new command task.
2. In the command task editor, enter the following shell command:

   echo "Hello, world!"
   

3. Save the command task and map it to a workflow.
4. When the workflow is run, the command task will execute the shell command and output "Hello, world!" to the console.

Pushdown optimization is a technique used in Informatica to optimize the performance of ETL processes by pushing down processing to the database level. By utilizing the processing power of the database, the amount of data transferred between the source and target systems is minimized, resulting in faster processing times. This optimization can be implemented through the use of SQL transformation, which allows the processing to occur on the database side, or through the use of a pushdown optimization option for individual transformations. Here's an example of using pushdown optimization for an SQL transformation:

SELECT *
FROM table_name
WHERE column_name = 'value'

By using this SQL query in an SQL transformation, the processing is pushed down to the database, which improves performance.

To handle multiple sources in a single mapping in Informatica, we can use a source qualifier transformation for each source and connect them to a joiner or union transformation. The joiner or union transformation can then be connected to downstream transformations and targets. Here's an example:

1. Add source definitions for each source in the mapping.
2. Add a source qualifier transformation for each source and connect them to the corresponding source definition.
3. Add a joiner or union transformation and connect the output of all source qualifier transformations to it.
4. Connect the joiner or union transformation to downstream transformations and targets.

Example code snippet:

   Source Definition 1 --> Source Qualifier 1 \
                                               --> Joiner/Union --> Target
   Source Definition 2 --> Source Qualifier 2 /

In Informatica, a mapping parameter is a read-only value that can be passed into the mapping at runtime, whereas a mapping variable is a value that can be read from and written to within the mapping. Mapping parameters are useful for providing values that do not change during the mapping, such as database connection details, while mapping variables can be used to store temporary values that may change during the mapping.

Here's an example of defining a mapping parameter in the Parameter tab of the Mapping Designer:

Name: Source_File_Name
Data Type: String
Default Value: 'input.txt'

And here's an example of defining a mapping variable in the Variables tab of the Mapping Designer:

Name: v_Total_Count
Data Type: Integer
Default Value: 0

Session partitioning is a technique in Informatica used to increase the session performance by distributing the input data across multiple threads for concurrent processing. To create a session partition, follow these steps:

1. Open the session you want to partition in the Workflow Manager.
2. Click on the "Properties" tab of the session.
3. In the "General Options" section, select the "Partitioning" option.
4. Choose the partition type, such as "Round Robin" or "Hash Auto-keys".
5. Specify the number of partitions and the partition key.
6. Validate and save the changes.

In Informatica, pre- and post-session commands allow you to perform tasks before and after a session runs. This can include tasks such as setting environment variables, running scripts, or sending email notifications. Pre-session commands are executed before the session starts, while post-session commands are executed after the session has completed. These commands can be specified at the workflow level or the session level. Here's an example of specifying pre- and post-session commands in a workflow using the pmcmd command line utility:

pmcmd startworkflow -sv MyService -d MyDomain -u MyUser -p MyPassword -f MyFolder -wait MyWorkflow
-precommand "echo Pre-session command executed"
-postcommand "echo Post-session command executed"

In Informatica, a static cache is created at design time and its content does not change during the session, while a dynamic cache is created at run time and can be updated during the session.

A static cache uses less memory and is faster to access, but it is less flexible because it cannot be updated during the session. A dynamic cache, on the other hand, uses more memory and may take longer to access, but it can be updated during the session, allowing for more flexible transformations.

Here is an example of creating a static cache:

SELECT * FROM CUSTOMER
CACHE ("CUST_CACHE", ROWSET_SIZE 10000)

And here is an example of creating a dynamic cache:

CREATE CACHE ("DYN_CACHE", ROWSET_SIZE 10000)

Note that in both cases, the ROWSET_SIZE parameter specifies the number of rows that can be cached at once.

A non-reusable transformation in Informatica is used when you want to use a transformation only once within a mapping. It cannot be reused across other mappings. The purpose of a non-reusable transformation is to have a transformation that is specific to a particular mapping, without the need for it to be used elsewhere. This can simplify the mapping design and make it easier to understand. To create a non-reusable transformation, simply select the "Non-reusable" option when creating a new transformation.

In Informatica, you can use the debugger to troubleshoot and test your mappings. To use the debugger, first, enable debugging for the session, then set breakpoints in the mapping where you want to start debugging. Run the session in debug mode, and the session will stop at each breakpoint. You can then step through the mapping, examine variable values, and view session data. Here's an example of enabling debugging for a session:

pmcmd startworkflow -sv MyIntegrationService -d MyDomain -f MyFolder -paramfile MyParameterFile.txt -wait MyWorkflow

pmcmd setworkflowparam -sv MyIntegrationService -d MyDomain -f MyFolder -paramfile MyParameterFile.txt -paramname $DBConnection -paramvalue MyDatabaseConnection

pmcmd settaskparam -sv MyIntegrationService -d MyDomain -f MyFolder -paramfile MyParameterFile.txt -paramname $SourceFile -paramvalue MySourceFile

pmcmd settaskparam -sv MyIntegrationService -d MyDomain -f MyFolder -paramfile MyParameterFile.txt -paramname $TargetFile -paramvalue MyTargetFile

pmcmd setoption -sv MyIntegrationService -d MyDomain -f MyFolder -paramfile MyParameterFile.txt -enabledebug 1

pmcmd starttask -sv MyIntegrationService -d MyDomain -f MyFolder -wait MySession

To handle errors and exceptions in an Informatica session, you can configure the session to stop or continue when it encounters errors. You can also configure error handling for individual transformations within the mapping. In case of an error, you can log the error details to a file or database, and send notifications via email or pager. Here is an example of how to configure the session to stop on errors:

<SessionConfig>
   <AllowErrorThreshold>1</AllowErrorThreshold>
   <OnTaskError>fail_task</OnTaskError>
   <StopOnErrors>true</StopOnErrors>
</SessionConfig>

This code will set the maximum number of allowed errors to 1, and the session will fail if it encounters more errors.

The Rank transformation in Informatica is used to select the top or bottom N number of rows based on a specified condition. The transformation assigns a rank to each row based on the condition, and then filters out the rows that do not meet the rank condition. It can be used to perform tasks such as selecting the highest paid employee or the top-selling product. Here is an example of using the Rank transformation to select the top 5 sales representatives based on their total sales:

Rank Sales_Rep_Total_Sales : RankPort
(
  Sales_Rep,
  Total_Sales,
  5,
  DESC
)

In this example, the transformation ranks the rows based on the Total_Sales column in descending order, and selects the top 5 rows based on this ranking.

In Informatica, a mapping is a visual representation of data flow between source(s) and target(s) with transformations, while a mapplet is a reusable set of transformations that can be used in multiple mappings. Mapplets are typically used for complex transformations that can be easily reused to ensure consistency across mappings. Mapplets can also have input/output parameters to make them more flexible.

Here is an example of a mapping that extracts data from a source, applies transformations, and loads it into a target:

Source -> Expression -> Filter -> Target

And here is an example of a mapplet that performs a simple transformation:

Input -> Expression -> Output

A dynamic lookup in Informatica is used to perform lookups on a table that is not predefined in the mapping. It allows you to specify the lookup table and the lookup condition at run time, which provides more flexibility in your mappings. To use a dynamic lookup, you need to create a mapping parameter to hold the lookup table name and a mapping variable to hold the lookup condition. Then, you can use these in the lookup transformation's properties to perform the dynamic lookup. Here's an example of how to use a dynamic lookup in Informatica:

SELECT employee_name, employee_department, employee_salary
FROM employee_table
WHERE employee_id = ?

In this example, the "?" placeholder represents the lookup condition that will be passed in at run time using a mapping variable.

In Informatica, a master source is a source system that is considered the most reliable and accurate source of data for a particular entity or subject area. It is used to maintain the consistency and accuracy of data across multiple systems. The master source contains the most current and complete information about the data, and any changes made to it will be reflected in all downstream systems. Examples of master sources could include a customer database, a product catalog, or an employee directory. Master sources are commonly used in data warehousing and business intelligence projects.

A Router transformation in Informatica is used to route data to different transformations or target tables based on specified conditions. It has one input group and multiple output groups, each with a condition defined using a set of rules. Data that matches the conditions is routed to the corresponding output group. It is useful in scenarios where different transformations or targets need to receive different subsets of data based on certain criteria. The following example shows a Router transformation with two output groups, one for orders from the USA and the other for orders from other countries:

Input data: Order number, Customer name, Country, Order amount

Router transformation:

Group1 (USA orders):
  Condition: Country='USA'
  Output ports: Order number, Customer name, Order amount

Group2 (Other orders):
  Condition: Country<>'USA'
  Output ports: Order number, Customer name, Country, Order amount

A Filter Transformation in Informatica is used to filter out the required data from the source data set based on some condition or criteria. The transformation can be used in both passive and active mode in a mapping. To use the Filter Transformation, drag and drop it onto the mapping designer canvas and connect the input and output ports. Then, in the Transformation Editor, specify the filter condition in the Filter condition tab. Here is an example of a filter transformation that filters out customers from a source table based on their age:

Source --> Filter_Transformation --> Target

Condition: AGE > 18

To create a parameter file for a session in Informatica, follow these steps:

1. Open a text editor and create a new file.
2. Add the parameter values with the format parameter=value, one per line.
3. Save the file with a .prm extension and place it in a directory accessible by the Informatica server.
4. In the session properties, set the Parameter Filename property to the full path and file name of the parameter file.

Here's an example of a parameter file:

$$DBConnectionName=MyDBConnection
$$InputFileName=/path/to/input/file.txt
$$OutputFileName=/path/to/output/file.txt

In the session properties, set the Parameter Filename property to the full path and file name of the parameter file, for example:

/opt/informatica/parameters/myparamfile.prm

The Workflow Monitor is a tool in Informatica used for monitoring the execution of workflows and sessions. It allows the user to view the status of workflows and sessions, check for errors, and troubleshoot issues. The Workflow Monitor displays information such as the start time, end time, and duration of the workflow or session, as well as the number of succeeded, failed, and rejected rows. Users can also perform tasks such as starting, stopping, and aborting workflows from the Workflow Monitor.

To open the Workflow Monitor, go to the Informatica Administrator tool, select the Workflow Manager, and then click on the Workflow Monitor tab.

The purpose of a merge transformation with sorted input in Informatica is to merge data from multiple sources based on a common key. The input data must be sorted based on the key to ensure that the merge operation works correctly. The merge transformation combines the input data into a single output stream based on the key, and can be used to consolidate data from multiple sources or to join data from different sources. Here is an example of a merge transformation in Informatica using two sorted input streams:

MERGE_EMP_DEPT:
    (DEPTNO, EMPNO, ENAME, SAL, COMM, JOB, MGR, HIREDATE, DEPTNAME)
    SORTED INPUT (EMP_SORTED, DEPT_SORTED)
    OUTPUT (MERGED_EMP_DEPT)
    GROUP BY (DEPTNO)

In Informatica, a connected transformation is used in a mapping where it receives input directly from the mapping pipeline. An unconnected transformation is used in a mapping where it is not connected directly to the mapping pipeline, but rather to other transformations. An unconnected transformation is called from another transformation or an expression, and it returns a value that is used by the calling transformation.

Here is an example of a connected transformation:

CREATE INFORMATICA TRANSFORMATION MyConnectedTransformation (
  inputPort1,
  inputPort2,
  outputPort1,
  outputPort2
)
DEFINE
/* Transformation logic */
END TRANSFORMATION;

Here is an example of an unconnected transformation:

CREATE INFORMATICA FUNCTION MyUnconnectedTransformation (
  inputParam1,
  inputParam2
)
RETURNS outputParam1 AS
DEFINE
/* Transformation logic */
END FUNCTION;

A reusable transformation is a transformation that can be used in multiple mappings while a non-reusable transformation is a transformation that can only be used in the mapping it was created in. Reusable transformations are saved in a shared folder and can be imported into other mappings. Non-reusable transformations are created directly in the mapping. The advantage of reusable transformations is that they can save development time and promote consistency across mappings, while the advantage of non-reusable transformations is that they are specific to a single mapping and can be customized to meet specific needs.

A surrogate key is a system-generated unique identifier assigned to a column in a table to act as a primary key. In Informatica, surrogate keys are used to create a mapping between a source and target system, typically in a data warehousing context. Surrogate keys are used to replace natural keys, which may not be unique or may change over time, making them unsuitable for reliable data retrieval. Surrogate keys can be generated using a sequence generator transformation or a surrogate key generator transformation.

Example of generating a surrogate key using a sequence generator transformation in Informatica:

CREATE SEQUENCE my_sequence
START WITH 1
INCREMENT BY 1;

SELECT NEXTVAL('my_sequence');

The purpose of a Stored Procedure Transformation in Informatica is to call a stored procedure from a relational database and pass input/output parameters to the stored procedure. This transformation can be used to perform complex calculations, data validations, or to update/insert data into the database. The stored procedure can be a system-defined or user-defined procedure. A Stored Procedure transformation can be used to define a reusable transformation within the mapping.

Here's an example of how to create a Stored Procedure Transformation in Informatica using a user-defined stored procedure:

1. Create a connection to the database in the Workflow Manager.
2. Create a mapping and add a Stored Procedure transformation.
3. In the Stored Procedure transformation, select the connection to the database.
4. Enter the stored procedure name and select the appropriate database schema.
5. Define the input and output parameters for the stored procedure.
6. Map the input/output ports of the Stored Procedure transformation to the appropriate ports in the mapping.
7. Save and validate the mapping.

A Source Qualifier transformation in Informatica is used to define the properties of the source data, such as database connections, SQL queries, and filter conditions. It is a mandatory transformation in any mapping that uses relational or flat file sources. The transformation performs a SQL query against the source database to retrieve the required data. The output data of the source qualifier transformation is used by subsequent transformations in the mapping. Here is an example of a source qualifier transformation query in Informatica:

SELECT employee_id, first_name, last_name, salary
FROM employee
WHERE salary > 50000;

The Rank transformation in Informatica is used to select the top or bottom N rows from a source based on a specified set of conditions. To use the Rank transformation, you need to create a mapping with a source and a Rank transformation. Configure the Rank transformation by specifying the number of rows you want to select and the port to use for ranking. You can also use the Rank index port to assign a rank to each row, and the Rank output port to pass the selected rows to the target. Here's an example of a Rank transformation in Informatica:

Source --> Rank Transformation --> Target

In this example, the Rank transformation selects the top 10 rows from the source based on a specified set of conditions and passes them to the target.

The purpose of a sequence generator transformation with reset option in Informatica is to generate unique sequence numbers for each row. The reset option allows the sequence number to restart from the initial value for each new group defined by a specified group by port. This is useful for generating separate sequence numbers for each group of related data.

Here's an example of using a sequence generator transformation with reset option in Informatica:

SEQUENCE_GENERATOR_1:
    Transformation Type: Sequence Generator
    Ports:
        - OUT_SEQUENCE: Output Port, Decimal(10,0)
        - RESET_PORT: Input/Output Port, Decimal(1,0)
    Properties:
        - NextVal = 1
        - IncrementBy = 1
        - Cycle = No
        - Reset = On
        - ResetValue = 1
        - GroupBy = RESET_PORT

In this example, the sequence generator transformation has two ports, OUT_SEQUENCE and RESET_PORT. The RESET_PORT is used as the group by port, so that the sequence number resets for each unique value in that port. The sequence generator starts at 1 and increments by 1 for each row.

In Informatica, an active transformation can change the number of rows that pass through it, while a passive transformation does not change the number of rows. Active transformations can alter the data flow, such as aggregating or filtering data, and require data to be sorted before processing. Passive transformations simply allow data to pass through unchanged, such as renaming columns or performing data type conversions.

Here is an example of a Filter transformation, which is an active transformation that filters out rows based on a condition:

IIF(Salary > 50000, 'Y', 'N')

And here is an example of an Expression transformation, which is a passive transformation that performs data type conversions:

TO_CHAR(HireDate, 'YYYY-MM-DD')

To handle multiple targets in a single mapping in Informatica, you can use a "Target Load Order" in the mapping. This allows you to specify the order in which the targets should be loaded.

You can create multiple Target Definitions in the mapping and link them to the corresponding Target Instances in the mapping. You can also create multiple Target Instances in the mapping to load the data into different targets.

Here is an example mapping with two targets:

Source --> Expression --> Filter --> Target1
                          |
                          --> Target2

In this example, the data flows from the source to an expression transformation, then through a filter transformation. The data is then split and loaded into two separate targets: Target1 and Target2.

The purpose of a Transaction Control transformation in Informatica is to control transaction boundary for a set of data rows. It can commit or rollback data based on specified conditions. It is used to commit data to the target table in a batch rather than committing each row individually, which can improve performance. The Transaction Control transformation has options to control how transactions are committed, such as committing data after a certain number of rows or committing data at the end of the transformation. Here is an example of using the Transaction Control transformation to commit data after a certain number of rows:

IIF(COUNT(*) >= 1000, TC_COMMIT_BEFORE, TC_CONTINUE_TRANSACTION)

To handle FTP file transfers in Informatica, you can use the FTP transformation. This transformation can read and write files from an FTP server. You need to configure the FTP connection and specify the remote directory and file name in the transformation properties. Below is an example of how to configure an FTP connection in Informatica:

FTP Host: ftp.example.com
FTP Port: 21
FTP Username: myusername
FTP Password: mypassword
Remote Directory: /path/to/remote/directory

Once the connection is established, you can use the transformation to read or write files from the FTP server.

A mapping parameter file in Informatica is used to define values for parameters used in a mapping. It allows users to define and set values for mapping parameters outside of the mapping itself. This makes it easier to change values for multiple mappings at once by simply modifying the parameter file. Here is an example of how to use a parameter file in Informatica:

[Global]
$Param1=123
$Param2=ABC

[Mapping]
$SourceFile=/path/to/source/file.csv
$TargetTable=TARGET_TABLE_NAME

In this example, the parameter file defines two global parameters, $Param1 and $Param2, as well as two mapping-specific parameters, $SourceFile and $TargetTable. These parameters can then be used within the mapping to define various properties and settings.

In Informatica, a persistent lookup cache is stored in a database table while a non-persistent lookup cache is stored in memory. Persistent lookup caches are used for large data sets that do not fit in memory and require frequent lookups. Non-persistent lookup caches are used for smaller data sets that can be easily cached in memory for faster lookups.

Here is an example of a persistent lookup cache in Informatica:

LOOKUP (lkp_customer)
  (SELECT * FROM customer_table)
  MATCH (customer_id)
  USING (db_connection)
  PERSISTENT CACHE (1000);

And here is an example of a non-persistent lookup cache in Informatica:

LOOKUP (lkp_product)
  (SELECT * FROM product_table)
  MATCH (product_id)
  USING (db_connection)
  CACHE (100);

To use a debugger in a workflow in Informatica, follow these steps:

1. Open the workflow you want to debug in the Workflow Manager.
2. Click on the "Debug" menu and select "Workflow".
3. Set breakpoints on the workflow, if necessary.
4. Start the debugger by clicking on the "Debug" button in the toolbar or by pressing F7.
5. Monitor the execution of the workflow using the "Debugger" tab and examine the data in the "Data Viewer" tab.

Here's an example of setting a breakpoint in a workflow:

1. Open the workflow in the Workflow Manager.
2. Right-click on the session you want to debug and select "Edit".
3. Click on the "Config Object" tab and select the "Properties" tab.
4. Check the "Enable Debugger" checkbox.
5. Save and close the session.
6. Start the debugger by running the workflow and selecting "Debug" from the run options.
7. The workflow will pause at the specified breakpoint, and you can use the Debugger and Data Viewer tabs to examine the workflow's execution and data.

A Java transformation in Informatica allows you to extend the functionality of the PowerCenter Integration Service by writing custom code in Java programming language. The Java code is used to transform data within a mapping. You can use a Java transformation to perform tasks that cannot be accomplished using built-in transformations. The Java transformation includes an Input/Output port, which provides input data and accepts output data. You can write Java code within the transformation to manipulate the input data and produce the output data. Here's an example of a Java transformation that converts a string to uppercase:

public void transform(JavaTransformationHelper helper) throws Exception {
    // Get input string
    String inputString = (String) helper.getInputRowData(0);

    // Convert string to uppercase
    String outputString = inputString.toUpperCase();

    // Set output data
    helper.setOutputRowData(new Object[] { outputString });
}

To handle real-time data integration in Informatica, you can use the Informatica Real-Time (RT) edition, which includes a set of components that allow you to capture and process real-time data. The Real-Time edition includes a specialized set of transformations, such as the Real-Time Source Qualifier and the Real-Time Expression Transformation. Additionally, you can use the Informatica PowerCenter Integration Service to capture and process data in real-time using the Change Data Capture (CDC) feature. The CDC feature enables you to capture changes to source data and apply those changes in real-time to the target system.

The Merge transformation in Informatica is used to merge data from two or more sources into a single target. It is particularly useful when the data sources have similar structures and are sorted in the same way. The Merge transformation can also be used to update existing records in the target, insert new records, or delete existing records based on certain conditions. The transformation requires sorted input, and it outputs a single data stream to the target. Here's an example of a Merge transformation in Informatica:

| Source1  |         |            |
|----------|         |  Merge     |       | Target |
| Emp ID   |         |  Emp ID    |       |--------|
| Emp Name |  ---->  |  Emp Name | ----> | Emp ID |
| Dept ID  |         |  Dept ID   |       |--------|
                       |  Salary   |

A mapping is a collection of source and target definitions, transformations, and mappings rules that describes the data flow from source to target. A mapplet is a set of transformations that can be used within a mapping or another mapplet. The main difference between a mapping and mapplet is that a mapping can run independently, whereas a mapplet must be used within a mapping. Mapplets provide a way to reuse the same set of transformations across multiple mappings, simplifying maintenance and reducing development time.

To handle complex data transformations in Informatica, one can use a combination of various transformations such as expression, aggregator, joiner, lookup, filter, router, rank, etc. Additionally, one can also use custom transformations such as Java transformation or stored procedure transformation to perform more complex logic. Using appropriate techniques like partitioning and pushdown optimization can also improve performance for large data sets. Proper testing and debugging techniques can ensure that the data transformations are performed accurately.

A dynamic pipeline partition in Informatica is used to distribute the data processing workload across multiple nodes or threads in a grid environment. It enables the session to automatically partition the data based on the partition key and dynamically create partitions based on the workload. This allows for parallel processing of data and improves performance. The partitioning is done based on rules defined in the partitioning tab of the session properties.

To use a stored procedure transformation in Informatica, follow these steps:

1. Create a stored procedure in your database.
2. In the Informatica Designer, create a mapping and add a Stored Procedure transformation.
3. Configure the Stored Procedure transformation by selecting the appropriate stored procedure and specifying the input and output ports.
4. Link the input and output ports of the transformation to other transformations in the mapping as needed.
5. Save and validate the mapping.

Here's an example of a Stored Procedure transformation in Informatica:

CREATE PROCEDURE my_stored_proc
    @input_param INT,
    @output_param VARCHAR(50) OUTPUT
AS
BEGIN
    SET @output_param = 'Result: ' + CAST(@input_param AS VARCHAR)
END

In the Informatica Designer, you would select "my_stored_proc" as the stored procedure to use in the transformation and configure the input and output ports accordingly.

The purpose of a web service transformation in Informatica is to consume or publish web services within a mapping. It allows integration with external applications by providing a way to communicate with them using web services. The transformation supports both Simple Object Access Protocol (SOAP) and Representational State Transfer (REST) web services. Input and output data can be mapped to the web service using ports and transformations. Here's an example of using the web service transformation to consume a SOAP web service:

<?xml version="1.0" encoding="UTF-8"?>
<WebSvcTransformation xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="../../xsd/WebSvcTransformation.xsd">
    <NAME>ConsumeWebService</NAME>
    <TRANSFORMATIONS>
        <TRANSFORMATION>
            <NAME>SourceDefinition</NAME>
            <TRANSFORMTYPE>Source Definition</TRANSFORMTYPE>
            <PORTS>
                <PORT>
                    <NAME>in_first_name</NAME>
                    <DATATYPE>string</DATATYPE>
                </PORT>
                <PORT>
                    <NAME>in_last_name</NAME>
                    <DATATYPE>string</DATATYPE>
                </PORT>
            </PORTS>
        </TRANSFORMATION>
        <TRANSFORMATION>
            <NAME>Web_Service</NAME>
            <TRANSFORMTYPE>Web Service</TRANSFORMTYPE>
            <PORTS>
                <PORT>
                    <NAME>in_first_name</NAME>
                    <DATATYPE>string</DATATYPE>
                    <DEFAULTVALUE>$$in_first_name</DEFAULTVALUE>
                </PORT>
                <PORT>
                    <NAME>in_last_name</NAME>
                    <DATATYPE>string</DATATYPE>
                    <DEFAULTVALUE>$$in_last_name</DEFAULTVALUE>
                </PORT>
                <PORT>
                    <NAME>out_email_address</NAME>
                    <DATATYPE>string</DATATYPE>
                </PORT>
            </PORTS>
            <WS_NAMESPACE>http://www.example.com/</WS_NAMESPACE>
            <WS_METHOD>getEmailAddress</WS_METHOD>
            <WS_ENDPOINT>http://www.example.com/EmailService</WS_ENDPOINT>
            <WS_PROTOCOL>SOAP</WS_PROTOCOL>
        </TRANSFORMATION>
        <TRANSFORMATION>
            <NAME>TargetDefinition</NAME>
            <TRANSFORMTYPE>Target Definition</TRANSFORMTYPE>
            <PORTS>
                <PORT>
                    <NAME>out_email_address</NAME>
                    <DATATYPE>string</DATATYPE>
                </PORT>
            </PORTS>
        </TRANSFORMATION>
    </TRANSFORMATIONS>
    <CONNECTORS>
        <CONNECTOR>
            <FROMINSTANCE>SourceDefinition</FROMINSTANCE>
            <FROMPORT>in_first_name</FROMPORT>
            <TOINSTANCE>Web_Service</TOINSTANCE>
            <TOPORT>in_first_name</TOPORT>
        </CONNECTOR>
        <CONNECTOR>
            <FROMINSTANCE>SourceDefinition</FROMINSTANCE>
            <FROMPORT>in_last_name</FROMPORT>
            <TOINSTANCE>Web_Service</TOINSTANCE>
            <TOPORT>in_last_name</TOPORT>
        </CONNECTOR>
        <CONNECTOR>
            <FROMINSTANCE>Web_Service</FROMINSTANCE>
            <FROMPORT>out_email_address</FROMPORT>
            <TOINSTANCE>TargetDefinition</TOINSTANCE>
            <TOPORT>out_email_address

To use a custom transformation in Informatica, follow these steps:

1. Create a new transformation in the PowerCenter Designer tool.
2. Select the "Custom" transformation type and provide the required information such as the transformation name, description, input and output ports, etc.
3. Develop the custom transformation code in the language supported by Informatica, such as Java or C++.
4. Build the custom transformation code into a dynamic link library (DLL) or shared object (SO) file.
5. Register the custom transformation in the Informatica repository using the Custom Transformation Manager.
6. Add the custom transformation to the mapping and configure it as required.

Here's an example of a custom transformation implemented in Java:

import com.informatica.powercenter.sdk.mapfwk.core.*;

public class MyCustomTransformation extends BaseTransform {
    @Override
    public void prepare() throws Exception {
        // Add any initialization code here
    }

    @Override
    public void transform() throws Exception {
        while (readNextRecord()) {
            // Implement the custom transformation logic here
            String input = getInputData(0).getString();
            String output = transformData(input);
            getOutputData(0).setString(output);
            incrementOutputRecordsCount();
        }
    }

    private String transformData(String input) {
        // Custom transformation logic goes here
        return input.toUpperCase();
    }
}

In Informatica, a target load plan is a set of instructions that specifies how data should be loaded into multiple targets. It can include different loading strategies, such as sequential or parallel loading. A target load order, on the other hand, specifies the order in which data should be loaded into a single target. It can be useful for ensuring data integrity or resolving dependencies between target tables.

Here is an example of a target load plan in Informatica:

<SESSION>
  <TASK SessionName="MySession">
    <MAPPING>
      <!-- My mapping logic -->
    </MAPPING>
    <TARGETLOADPLAN>
      <GROUP Name="TargetGroup1">
        <TARGET Name="Target1"/>
        <TARGET Name="Target2"/>
      </GROUP>
      <GROUP Name="TargetGroup2">
        <TARGET Name="Target3"/>
      </GROUP>
      <!-- Additional loading strategies -->
    </TARGETLOADPLAN>
  </TASK>
</SESSION>

And here is an example of a target load order:

<SESSION>
  <TASK SessionName="MySession">
    <MAPPING>
      <!-- My mapping logic -->
    </MAPPING>
    <TARGETORDER>
      <TARGET Name="Target1"/>
      <TARGET Name="Target2"/>
      <TARGET Name="Target3"/>
      <!-- Additional target order logic -->
    </TARGETORDER>
  </TASK>
</SESSION>

To handle complex business rules in Informatica, we can use a combination of transformations such as Router, Filter, Expression, and Lookup. We can also use custom transformations and stored procedures. Additionally, we can create business rules using the Informatica Rule Engine and integrate them into the mapping. Here's an example of using the Lookup transformation to handle complex business rules:

SELECT *
FROM lookup_table
WHERE column1 = input_column1
  AND column2 = input_column2
  AND column3 = input_column3

This Lookup transformation can be used to retrieve data from a lookup table based on multiple input columns, and then apply complex business rules to the output.

Constraint-based loading in Informatica is used to define and enforce business rules and constraints during the ETL process. It ensures that only valid data is loaded into the target system by checking the constraints defined in the mapping or workflow. For example, a constraint can be defined to check if the incoming data matches a certain pattern or value before it is loaded into the target. Constraint-based loading is implemented using various transformations such as Filter, Router, and Expression. The constraints can be defined using SQL queries or transformation logic.

A post-session command in Informatica is used to execute a shell command or a batch file after a session completes. To use a post-processing command, navigate to the session properties in the Workflow Manager, and in the Config Object tab, enter the shell command or batch file path in the Post-Session Success Command or Post-Session Failure Command field. For example, to run a shell script after the session completes successfully, enter the following command:

sh /path/to/script.sh

This can be useful for tasks such as sending notification emails or moving processed files to a different directory.

Pushdown optimization for partitions is a technique used in Informatica to improve the performance of data integration processes by pushing down data transformations to the database level. This technique can reduce the amount of data transferred between Informatica and the database, resulting in faster processing times. By using pushdown optimization, the database engine can take advantage of its native processing power and resources to perform transformations, which can significantly reduce the processing time for large volumes of data. Pushdown optimization can be configured for each partition in the mapping, allowing for granular control over the optimization process.

Change Data Capture (CDC) is used in Informatica to identify and capture changes made to the source data. CDC can be used to track changes to a specific table or set of tables, and the captured changes can be propagated to target systems for real-time data integration. The CDC process involves creating a CDC source definition, adding a CDC table to the mapping, and configuring the CDC options. The following code snippet shows an example of a CDC mapping that uses a CDC source definition to capture changes to the EMPLOYEE table:

1. Create a CDC source definition for the EMPLOYEE table.
2. Add a CDC table to the mapping.
3. Configure the CDC options for the mapping.
4. Propagate the changes to the target system using a target definition.

The high precision mode in Informatica increases the precision and scale of decimal and numeric data types during data integration. It enables higher accuracy in calculations and maintains the integrity of the data. High precision mode is especially useful when dealing with financial or scientific data that requires precise calculations. To use high precision mode, you can select the "High Precision" option in the session properties or in the mapping properties. Here is an example of selecting the high precision mode in a mapping:

<?xml version="1.0"?>
<PO_MAPPING>
    <MAPPING NAME="PO_MAPPING" VERSION="1">
        <TRANSFORMATION NAME="EXPR_TRANSFORM" TYPE="Expression">
            <GROUP NAME="OUTPUT">
                <OUTPUTFIELD NAME="PO_AMOUNT" DATATYPE="NUMBER" SCALE="2" PRECISION="10"/>
            </GROUP>
            <EXPRTRANSFORMATIONS>
                <EXPRTRANSFORMATION PORTTYPE="OUTPUT" EXPRESSION="po_qty * po_price" PORTNAME="PO_AMOUNT"/>
            </EXPRTRANSFORMATIONS>
        </TRANSFORMATION>
    </MAPPING>
</PO_MAPPING>

To use a Java transformation with an external library in Informatica, you need to follow these steps:

1. Place the external library JAR file in the $INFA_HOME/services/shared/jars/custom folder.
2. Restart the Informatica services.
3. In the Java transformation, import the necessary classes from the external library and use them in the code.

Example code to use external library classes in a Java transformation:

import com.example.externalLibrary.ExternalClass;

public class MyJavaTransformation extends JavaTransformation {

  public void transform() throws Exception {
    // Use an instance of the external class in the transformation logic
    ExternalClass externalObj = new ExternalClass();
    // perform some operation with the external class
    ...
  }

}

A Source Qualifier transformation in Informatica is used to represent the rows that are extracted from the source. Source Qualifier Override is used to modify the SQL query generated by the Source Qualifier transformation to override the default behavior. It can be used to add filters or custom queries to fetch specific rows from the source. This can be useful in situations where you want to read a subset of data or filter out unwanted data before it is processed further. Here's an example of using a Source Qualifier Override:

SELECT * FROM CUSTOMER WHERE STATE = 'CA';

Real-time pushdown optimization in Informatica allows mapping logic to be executed on the target database engine instead of the Informatica server, thereby reducing data movement and improving performance. This can be achieved by selecting the "Pushdown Optimization" option in the mapping configuration and using a compatible database system, such as Oracle or Teradata. Code snippets for a pushdown SQL query transformation in an Oracle database might look like:

-- Mapping SQL override
SELECT emp_id, emp_name FROM employee WHERE hire_date > sysdate-365;

-- Pushdown SQL query
SELECT emp_id, emp_name FROM employee WHERE hire_date > TRUNC(SYSDATE) - 365;

In Informatica, a session is a unit of work that extracts data from sources, transforms it, and loads it into targets, while a workflow is a collection of tasks that can include one or more sessions. A workflow manages the execution and dependency of the tasks, and can include control flows and scheduling options. Sessions can be run as part of a workflow, and can be configured with various options such as pre- and post-session commands, session properties, and mapping parameters. Here is an example of running a session within a workflow:

Workflow wf_example
   Session Task_Session_1
   Session Task_Session_2
   Control flow
      Task_Session_1 -> Task_Session_2
   End workflow

Parallel processing in Informatica can be handled by using partitions. You can partition data into multiple smaller chunks and process them in parallel to speed up the ETL process. You can use the Partitioning tab in the session properties to define the number of partitions and the partition key. You can also use dynamic partitioning to partition data based on runtime conditions. Here's an example of using dynamic partitioning:

IIF(ISNULL($$PARTITION_NUM), MOD($$NUMPARTITIONS,2) + 1, $$PARTITION_NUM)

This expression partitions data based on whether the $$PARTITION_NUM parameter has a value. If it's null, it uses the modulus of the number of partitions and 2 to determine the partition number, otherwise, it uses the $$PARTITION_NUM parameter.

The purpose of the Normalizer transformation in Informatica is to normalize a column of data into multiple rows based on a delimiter. It can also denormalize data by combining multiple rows into a single row with repeated data. This transformation is particularly useful when dealing with flat file data sources where a single row may contain multiple values that need to be separated and processed individually.

Here is an example of using the Normalizer transformation to split a comma-separated string into multiple rows:

Input data:
ID,Name,Phone
1,John,555-1234
2,Jane,555-5678

Output data:
ID,Name,Phone
1,John,555-1234
2,Jane,555-5678

After applying Normalizer transformation on the Phone column, the output will be:

ID,Name,Phone
1,John,555
1,John,1234
2,Jane,555
2,Jane,5678

A SQL transformation in Informatica is used to perform SQL operations on data within the mapping. The SQL transformation can execute SQL statements such as SELECT, INSERT, UPDATE, and DELETE. The SQL transformation can also be used to call stored procedures. The SQL transformation requires a connection to a database to execute SQL statements. Below is an example SQL statement that can be used in a SQL transformation:

SELECT col1, col2, col3 FROM table1 WHERE col1 > 10;

A joiner transformation in Informatica combines data from two or more sources based on a common key. When a joiner transformation uses a sorted input, it can take advantage of joiner cache. The purpose of a joiner cache is to store the sorted data in memory, allowing for faster join operations. Joiner cache can be enabled by selecting the "Sorted Input" option and choosing a cache type in the joiner transformation properties. Here's an example code snippet of a joiner transformation using sorted input and joiner cache:

SELECT EMPLOYEE_NAME, DEPARTMENT_NAME
FROM EMPLOYEE, DEPARTMENT
WHERE EMPLOYEE.DEPARTMENT_ID = DEPARTMENT.DEPARTMENT_ID

In the above example, the joiner transformation will use a joiner cache to speed up the join operation.

Dynamic data partitioning in Informatica can be handled using partition key, which is a column that splits data based on a specific condition. The partition key can be set dynamically using an expression or variable. The partition key value can be generated at run-time based on the data being processed. The following code snippet shows how to use a dynamic partition key using an expression:

IIF(PARTITION_COLUMN='A',1,2)

In this example, the partition key is set based on the value of the partition column. If the partition column value is 'A', then the partition key is set to 1, otherwise, it is set to 2. This allows data to be split dynamically based on the value of the partition column.

In Informatica, an event-based schedule is triggered by an external event such as file arrival or database updates, while a time-based schedule runs at a scheduled time interval.

An event-based schedule uses event-wait task to wait for an external event to occur, while a time-based schedule uses a timer task to execute the workflow at the specified interval.

Example of an event-based schedule:

<EventWait Task>
  <EventDetails>
    <WaitForEvent Name="FileArrival" Type="FILE"/>
    <WaitForEvent Name="DBUpdate" Type="DATABASE"/>
  </EventDetails>
  <Task Name="MyTask">
    <Command></Command>
  </Task>
</EventWait>

Example of a time-based schedule:

<Timer Task>
  <TimerDetails>
    <StartTime>2019-01-01 00:00:00</StartTime>
    <EndTime>2019-12-31 23:59:59</EndTime>
    <RepeatInterval>30</RepeatInterval>
    <Unit>Minute</Unit>
  </TimerDetails>
  <Task Name="MyTask">
    <Command></Command>
  </Task>
</Timer>

To use a flat file target in Informatica, you need to create a new target definition and specify the file properties, such as file format, delimiter, and file path. Then, create a mapping and add the flat file target as the target instance. Finally, create a session and configure the session properties, such as target connection and file write mode. Below is an example of creating a flat file target in Informatica:

1. Create a new target definition with the file properties:

Target Definition Name: Employee_Flat_File
Target Type: File
File Directory: /home/user/output
File Name: employee.txt
File Type: Delimited
Field Delimiter: ,

1. Add the flat file target to the mapping as the target instance:

Mapping: Employee_Data_Load
Source: Employee_Source
Target: Employee_Flat_File

1. Create a session and configure the session properties, such as target connection and file write mode:

Session Name: Employee_Load_Session
Target Load Order: Employee_Flat_File
Target Connection: file_connection
File Write Mode: Truncate

Dynamic mapping is a feature in Informatica that enables the creation of mappings at runtime. This allows for greater flexibility in data integration processes where the structure of the data changes frequently. It also reduces development time as the mappings do not need to be created manually. Dynamic mapping involves the use of mapping templates, which are parameterized mappings that can be configured to accept different sources, targets, and transformations. These templates can then be instantiated with specific source and target schemas at runtime. The following code snippet shows an example of a dynamic mapping template in Informatica:

<TRANSFORMATION NAME="Dynamic Mapping Template" TYPE="Source Qualifier">
  <TRANSFORMFIELD DATATYPE="string" DEFAULTVALUE="" DESCRIPTION="" NAME="Field1" PORTTYPE="OUTPUT"/>
  <TRANSFORMFIELD DATATYPE="string" DEFAULTVALUE="" DESCRIPTION="" NAME="Field2" PORTTYPE="OUTPUT"/>
  <TABLEATTRIBUTE NAME="Sql Query" VALUE="SELECT &lt;%=config.field1%&gt;, &lt;%=config.field2%&gt; FROM &lt;%=config.source_table%&gt;"/>
</TRANSFORMATION>

External Loader transformation in Informatica is used to load data into the target database using the external loader utility provided by the database vendor. To use the External Loader transformation in Informatica, you need to configure the target database and create an external loader control file that specifies the format of the input data. Then, you need to create an External Loader transformation in the mapping and configure it to use the external loader control file. Here's an example of External Loader transformation in a mapping:

rustCopy code
| Source Qualifier | -> | External Loader Transformation | -> | Target |

In the External Loader transformation, you need to specify the location of the external loader control file and the database connection information.

In Informatica, a shortcut is a reference to an existing object in the repository, which allows you to reuse an object in multiple workflows or mappings. The purpose of a shortcut is to save time and effort by reducing the need to create and maintain duplicate objects. When you create a shortcut, any changes made to the original object are automatically reflected in all workflows or mappings that use the shortcut. To create a shortcut in Informatica, you can right-click on the object in the repository and select "Create Shortcut," or use the "Create Shortcut" option in the "Actions" menu.

To handle complex mappings in Informatica, you can follow some best practices such as breaking down the mapping into smaller, logical parts, using reusable transformations, and optimizing performance. Additionally, you can use techniques such as partitioning and caching to improve performance. Here is an example of how to use a reusable transformation in a mapping:

1. Create a reusable transformation:

[Transformation: my_transform]
Input Ports: in_field1, in_field2
Output Ports: out_field1, out_field2
Transformation logic:
out_field1 = in_field1 + in_field2
out_field2 = in_field1 - in_field2

1. Use the reusable transformation in a mapping:

[Mapping: my_mapping]
Source: my_source
Target: my_target
Transformation: my_transform(in_field1, in_field2, out_field1, out_field2)

This will use the my_transform transformation in the my_mapping mapping, with the specified input and output ports.

Pre-session and post-session variable assignments are used to initialize or update session-level variables before or after a session is run in Informatica. Pre-session variables are used to set default values for session-level variables before the session starts. Post-session variables are used to update the value of session-level variables after the session completes. This allows for dynamic changes to be made to session-level variables based on the results of the session.

For example, the following code snippet sets a pre-session variable $$FILENAME to a default value of "input_file.txt" and updates a post-session variable $$ROWCOUNT with the total number of rows processed by the session:

Pre-Session Command:
$$FILENAME=input_file.txt

Post-Session Command:
let $$ROWCOUNT = $PMTotalTargetRows

The XML transformation in Informatica is used to convert XML data into relational or flat file format or vice versa. To use this transformation, add it to the mapping, configure its properties, and connect the appropriate ports.

Below is an example of using an XML transformation to extract data from an XML file and write it to a flat file target:

<?xml version="1.0" encoding="UTF-8"?>
<PO>
  <PONumber>12345</PONumber>
  <PODate>2023-02-24</PODate>
  <Items>
    <Item>
      <ItemID>001</ItemID>
      <Description>Product A</Description>
      <Quantity>10</Quantity>
    </Item>
    <Item>
      <ItemID>002</ItemID>
      <Description>Product B</Description>
      <Quantity>5</Quantity>
    </Item>
  </Items>
</PO>

In the mapping, connect the XML source to the XML transformation and configure the transformation to extract data from the appropriate elements. Connect the transformation to a flat file target and configure the target to write the data in the desired format.

The purpose of a Joiner Transformation with sorted input in Informatica is to join two sorted input streams based on a common key. This type of join can be performed faster because it requires less memory and disk I/O than a regular join transformation. The Joiner Transformation requires both inputs to be sorted in the same order, which can be achieved by using Sorter Transformations before the Joiner Transformation. Here is an example of a Joiner Transformation with sorted input in Informatica:

SELECT *
FROM employee e, department d
WHERE e.dept_id = d.dept_id

In this example, the Joiner Transformation is used to join the Employee and Department tables based on their common department ID. The Joiner Transformation requires both inputs to be sorted by department ID before it can perform the join.

Data masking in Informatica is used to hide sensitive data, such as credit card numbers, social security numbers, and other confidential information. This is done to ensure that the data is protected and cannot be accessed by unauthorized users. In Informatica, data masking can be achieved through the use of the Masking Transformation or the Data Masking option in the PowerCenter Designer tool. Masking rules can be defined and applied to specific fields or columns in the data, and various masking techniques, such as substitution and shuffling, can be used to ensure that the masked data is still valid but does not reveal sensitive information.

In Informatica, a persistent dynamic cache stores data in a file or a database table, which can be reused across multiple sessions, whereas a non-persistent dynamic cache stores data in memory and is deleted once the session ends. Persistent cache can be useful when there is a requirement to share data across sessions, and non-persistent cache can be useful when dealing with large data sets that can be cached in memory. Below is an example of creating a persistent dynamic cache in Informatica:

1. Create a cache definition using the "Dynamic Cache" option in the Source Analyzer.
2. Choose the "Persistent" option in the "Cache Type" field and specify the connection details for the database or file.
3. Map the source columns to the cache columns in the mapping.
4. Add the "Lookup" transformation and select the cache definition as the lookup source.
5. Configure the lookup transformation to use the cache for storing and retrieving data.

A parameter file is used to define the values of parameters used in a mapping or a session in Informatica. To use a parameter file for dynamic mappings, follow these steps:

1. Create a parameter file in a text editor and define the values for each parameter. For example:

$$SourceFile=/input/sourcefile.csv
$$TargetFile=/output/targetfile.csv
$$Date=2023-02-24

1. In the mapping or session, define the parameter names with a prefix "$$" followed by the parameter name, such as "$$SourceFile".
2. In the session properties, specify the parameter file under the "Parameter File" tab.

When the session is executed, the values defined in the parameter file will be used for the corresponding parameters in the mapping or session. This allows for easy modification of parameter values without having to modify the mapping or session directly.

A worklet is a reusable set of tasks or workflow designed to perform a specific unit of work. It is a collection of one or more tasks that perform a specific function and can be executed as a standalone unit. Worklets are used to simplify the process of designing and maintaining workflows, reduce redundancy, and improve the overall efficiency of the workflow. They are particularly useful for complex or repetitive tasks that are used across multiple workflows. Worklets can be used within workflows, other worklets or standalone to execute a specific task or set of tasks.

Example:

<WORKLET DESCRIPTION="Sample worklet" NAME="WL_SAMPLE">
    <TASKINSTANCE DESCRIPTION="Sample session" NAME="s_SAMPLE">
        <MAPPINGNAME>MAP_SAMPLE</MAPPINGNAME>
        <TASKTYPE>Session</TASKTYPE>
        <CONFIGREFERENCE>SC_SAMPLE</CONFIGREFERENCE>
        <REUSABLE>YES</REUSABLE>
    </TASKINSTANCE>
</WORKLET>