10 Informatica Interview Questions and Answers in 2023
1. How do you optimize performance of an Informatica workflow?
Optimizing performance of an Informatica workflow involves a number of steps.
1. Analyze the source data: The first step is to analyze the source data. This includes understanding the data structure, data types, and the number of records. This will help identify any potential performance bottlenecks.
2. Optimize the mapping: The next step is to optimize the mapping. This includes using the most efficient transformation logic, minimizing the number of transformations, and using the most efficient join logic.
3. Tune the session: The next step is to tune the session. This includes setting the appropriate buffer size, using the most efficient session properties, and setting the appropriate commit interval.
4. Monitor the workflow: The final step is to monitor the workflow. This includes monitoring the workflow logs, performance metrics, and system resources. This will help identify any potential performance issues.
By following these steps, an Informatica developer can optimize the performance of an Informatica workflow.
2. Describe the process of creating a mapping in Informatica.
Creating a mapping in Informatica involves several steps.
1. First, you need to create a source definition. This is the definition of the source data that you will be using in the mapping. You can create a source definition by connecting to the source database and selecting the tables and columns that you want to use.
2. Next, you need to create a target definition. This is the definition of the target data that you will be writing to. You can create a target definition by connecting to the target database and selecting the tables and columns that you want to use.
3. After that, you need to create a mapping. This is the actual mapping of the source and target data. You can create a mapping by dragging and dropping the source and target definitions onto the mapping canvas. You can then use the transformation objects to transform the data as needed.
4. Finally, you need to create a session. This is the definition of how the mapping will be executed. You can create a session by selecting the mapping, source, and target definitions that you want to use. You can then configure the session properties such as the number of threads, the commit interval, and the error handling.
Once you have completed these steps, you can execute the session to run the mapping.
3. What is the difference between a source qualifier and a joiner transformation?
A Source Qualifier transformation is an active and connected transformation that is used to read data from a relational or a flat file source. It allows you to select the source table or view, join multiple sources, and filter the data. It also allows you to specify the source data type and the sort order of the data.
A Joiner transformation is an active and connected transformation that is used to join two or more sources. It allows you to join two or more sources based on a common field. It also allows you to specify the join type (inner, outer, etc.), the join condition, and the join order. It also allows you to specify the output port type and the sort order of the data.
4. How do you debug an Informatica workflow?
Debugging an Informatica workflow involves a few steps.
First, you should check the workflow log to identify any errors or warnings that may have occurred. This log can be found in the workflow monitor. It will provide information about the workflow run, such as the start and end times, the number of rows processed, and any errors or warnings that occurred.
Second, you should check the session log to identify any errors or warnings that may have occurred during the session run. This log can be found in the session monitor. It will provide information about the session run, such as the start and end times, the number of rows processed, and any errors or warnings that occurred.
Third, you should check the mapping log to identify any errors or warnings that may have occurred during the mapping run. This log can be found in the mapping monitor. It will provide information about the mapping run, such as the start and end times, the number of rows processed, and any errors or warnings that occurred.
Fourth, you should check the source and target databases to identify any errors or warnings that may have occurred during the data transfer. This can be done by running a query on the source and target databases to check for any errors or warnings.
Finally, you should check the Informatica server logs to identify any errors or warnings that may have occurred during the workflow run. This log can be found in the server log viewer. It will provide information about the server run, such as the start and end times, the number of rows processed, and any errors or warnings that occurred.
By following these steps, you should be able to identify and debug any errors or warnings that may have occurred during the Informatica workflow run.
5. What is the purpose of a lookup transformation?
The purpose of a Lookup transformation in Informatica is to look up data in a reference table, view, or synonym. It can be used to perform a variety of tasks, such as validating source data, retrieving related data from a reference table, and performing calculations.
The Lookup transformation can be used to look up data in a relational table, view, or synonym. It can also be used to look up data in a flat file. The Lookup transformation can be used to perform a variety of tasks, such as validating source data, retrieving related data from a reference table, and performing calculations.
The Lookup transformation can be configured to return a single row or multiple rows. It can also be configured to return all rows or only rows that meet certain criteria. The Lookup transformation can be used to perform a variety of tasks, such as validating source data, retrieving related data from a reference table, and performing calculations.
The Lookup transformation can also be used to perform a variety of tasks, such as validating source data, retrieving related data from a reference table, and performing calculations. It can also be used to perform a variety of tasks, such as validating source data, retrieving related data from a reference table, and performing calculations. It can also be used to perform a variety of tasks, such as validating source data, retrieving related data from a reference table, and performing calculations.
6. How do you handle errors in an Informatica workflow?
When handling errors in an Informatica workflow, the first step is to identify the source of the error. This can be done by examining the workflow log and the session log. Once the source of the error is identified, the next step is to determine the cause of the error. This can be done by examining the data in the source and target systems, as well as the mapping logic used in the workflow.
Once the cause of the error is identified, the next step is to determine the best way to resolve the error. This can be done by making changes to the mapping logic, or by adding additional transformation rules to the workflow. It is also important to ensure that any changes made to the workflow do not introduce new errors.
Finally, once the error is resolved, it is important to test the workflow to ensure that the error has been resolved and that the workflow is functioning as expected. This can be done by running a test session and examining the results.
7. What is the difference between a connected and unconnected lookup transformation?
A connected lookup transformation is used to look up data that is stored in a relational table, view, or synonym. It is connected to the data flow by a pipeline and can be used to retrieve data from the source or to perform a lookup operation. The lookup transformation can be used to perform a lookup operation on a single row or multiple rows.
An unconnected lookup transformation is used to look up data that is stored in a flat file or a stored procedure. It is not connected to the data flow by a pipeline and can only be used to perform a lookup operation. The lookup transformation can be used to perform a lookup operation on a single row or multiple rows. Unlike a connected lookup transformation, an unconnected lookup transformation does not return data to the data flow. Instead, it returns a single value or multiple values to the transformation or mapping in which it is used.
8. How do you handle slowly changing dimensions in Informatica?
In Informatica, slowly changing dimensions (SCDs) are used to track changes in the data over time. There are three types of SCDs: Type 1, Type 2, and Type 3.
Type 1 SCD: This type of SCD overwrites existing data with new data. This is the simplest type of SCD to implement, but it does not keep a history of changes.
Type 2 SCD: This type of SCD keeps a history of changes by creating new records for each change. This is the most commonly used type of SCD, as it allows for a full history of changes to be tracked.
Type 3 SCD: This type of SCD keeps a history of changes by adding new columns to the existing record. This is the most complex type of SCD to implement, but it allows for a full history of changes to be tracked without creating new records.
To handle slowly changing dimensions in Informatica, I would use the SCD transformation. This transformation allows you to specify which type of SCD you want to use, as well as the columns that should be used to identify changes. It also allows you to specify how to handle existing records when new data is encountered. Once the SCD transformation is configured, it can be used to track changes in the data over time.
9. What is the purpose of a target load plan?
The purpose of a target load plan is to define the data loading strategy for a target system. It outlines the steps and processes that need to be taken in order to successfully load data into the target system. This plan should include the source of the data, the target system, the mapping of the data, the transformation of the data, the loading of the data, and the verification of the data. The target load plan should also include any error handling and recovery processes that need to be taken in the event of a data loading failure. The target load plan should be designed to ensure that the data is loaded accurately and efficiently into the target system.
10. How do you ensure data quality in an Informatica workflow?
Ensuring data quality in an Informatica workflow is a multi-step process.
First, I would use the Data Quality Transformation to identify and remove any invalid or incorrect data. This transformation can be used to check for data type, length, format, and range errors. It can also be used to check for duplicate records, missing values, and incorrect values.
Second, I would use the Data Masking Transformation to mask sensitive data. This transformation can be used to replace sensitive data with random values or with values from a lookup table.
Third, I would use the Data Profiling Transformation to analyze the data and identify any patterns or anomalies. This transformation can be used to identify outliers, missing values, and incorrect values.
Fourth, I would use the Data Validation Transformation to validate the data against a set of rules. This transformation can be used to check for data type, length, format, and range errors.
Finally, I would use the Data Cleansing Transformation to cleanse the data. This transformation can be used to remove duplicate records, replace incorrect values, and fill in missing values.
By using these transformations, I can ensure that the data is of high quality and is ready for use in the Informatica workflow.