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The Ultimate Guide for Informatica Interview Questions

Informatica is a leading data integration and management tool widely used by organizations to handle complex data processing and ETL (Extract, Transform, Load) tasks. If you’re preparing for an Informatica job interview, it’s crucial to be well-versed in both the theoretical and practical aspects of the platform. This comprehensive guide on Informatica interview questions and answers covers a range of topics, from basic concepts to advanced techniques, ensuring you are well-prepared to impress your interviewer.

In this article, we delve into:

  • Fundamental Concepts: Understanding the core architecture of Informatica PowerCenter, including its components and data integration capabilities.
  • ETL Processes: Detailed explanations of how to design and execute ETL workflows, handle data transformations, and manage data quality.
  • Advanced Transformations: Insights into advanced transformations like Lookup, Joiner, Rank, and Router, with practical examples and scenarios.
  • Performance Optimization: Tips and tricks to enhance the performance of your Informatica mappings and workflows, including session partitioning and pushdown optimization.
  • Error Handling: Strategies for implementing robust error handling mechanisms within your Informatica workflows to ensure data integrity and process reliability.
  • Real-World Scenarios: Answers to common real-world scenarios and challenges you might face while working with Informatica, along with best practices for overcoming them.
  • Latest Features: An overview of the latest features and updates in Informatica, ensuring you stay current with the platform’s advancements.

Whether you are a beginner or an experienced professional, this guide provides valuable insights and practical knowledge to help you confidently navigate your Informatica job interview. With detailed explanations, practical examples, and expert tips, you’ll be well-equipped to answer any question that comes your way and secure your desired position in the field of data integration and management.

Q1. How can we update a record in the target table without using Update Strategy?
Ans: One way to update records in the target table without using Update Strategy is by utilizing the “Update Override” feature in the session properties. By specifying an SQL override in the target definition, you can instruct the session to perform updates based on certain conditions directly within the mapping itself. For example, you can write an SQL override like:

UPDATE target_table
SET column1 = :NEW_value
WHERE condition;

This allows you to update specific records in the target table based on your criteria directly within the mapping without relying on the Update Strategy transformation.

Q2. How to use PowerCenter Command-Line in Informatica?
Ans: PowerCenter Command-Line Interface (pmcmd) is used to interact with the Informatica server to start, stop, or query the status of workflows and sessions. Here’s how you can use it:

  • To start a workflow: pmcmd startworkflow -sv <Integration_Service> -d <Domain_Name> -u <Username> -p <Password> -f <Folder_Name> -w <Workflow_Name>
  • To stop a workflow: pmcmd stopworkflow -sv <Integration_Service> -d <Domain_Name> -u <Username> -p <Password> -f <Folder_Name> -w <Workflow_Name>
  • To check workflow/session status: pmcmd getsessionstatistics -sv <Integration_Service> -d <Domain_Name> -u <Username> -p <Password> -f <Folder_Name> -w <Workflow_Name>

Q3. Define parallel processing?
Ans: Parallel processing in Informatica refers to the ability to execute multiple tasks concurrently, thereby improving performance and efficiency. In a parallel processing environment, data transformation and loading tasks are divided into smaller units, which are then executed simultaneously across multiple CPU cores or nodes. This allows for faster processing of large volumes of data.

Q4. Differentiate between various types of schemas in data warehousing?
Ans: In data warehousing, there are three main types of schemas:

  • Star Schema: This schema consists of one or more fact tables referencing any number of dimension tables. It resembles a star, with the fact table at the center and dimension tables surrounding it.
  • Snowflake Schema: Similar to the star schema, but the dimension tables are normalized into multiple related tables, forming a shape resembling a snowflake.
  • Galaxy Schema: Also known as a fact constellation schema, it consists of multiple fact tables sharing dimension tables. It is more complex than star and snowflake schemas.

Q5. Explain sessions and shed light on how batches are used to combine executions?
Ans: In Informatica, a session is a task that executes the ETL (Extract, Transform, Load) logic defined in a mapping. Batching in sessions refers to the process of grouping multiple source rows into smaller sets, which are then processed together. This is useful for optimizing memory usage and improving performance, especially when dealing with large datasets. Batches are used to combine executions by allowing the session to process data in manageable chunks rather than processing all data at once.

Q6. What is XML Source Qualifier Transformation in Informatica?
Ans: The XML Source Qualifier Transformation in Informatica is used to read data from XML files as a source in mappings. It extracts data from XML elements and attributes and converts it into a relational format that can be processed by other transformations in the mapping. This transformation provides flexibility in handling complex XML structures and enables integration of XML data into the ETL process.

Q7. What are the types of Lookup transformations?
Ans: There are two types of Lookup transformations in Informatica:

  • Connected Lookup: Performs lookup operation in the same data flow pipeline as the source qualifier. It returns a single value for each input row.
  • Unconnected Lookup: Performs lookup operation in a separate data flow pipeline, typically within an expression or another transformation. It can return multiple values for each input row.

Q8. What is incremental aggregation?
Ans: Incremental aggregation is a technique used to perform aggregation operations (such as sum, average, count) on a subset of data, rather than on the entire dataset. It involves updating aggregate values incrementally by processing only the new or changed data since the last aggregation. This helps improve performance by reducing the amount of data that needs to be processed during each aggregation operation.

Q9. What is the functionality of F10 in Informatica?
Ans: In Informatica PowerCenter, pressing F10 while in the mapping designer allows you to validate the mapping. It checks for errors, warnings, and consistency issues in the mapping design and provides feedback on any issues that need to be addressed before the mapping can be executed.

Q10. How do you handle duplicate records in Informatica?
Ans: Duplicate records in Informatica can be handled using various methods:

  • Using Sorter Transformation: Sort the data based on the key fields and then use the “Remove Duplicates” option in the Sorter transformation to eliminate duplicate records.
  • Using Aggregator Transformation: Use the Aggregator transformation to group records by key fields and perform aggregation operations. Duplicate records will be automatically eliminated during the aggregation process.
  • Using Expression Transformation: Use the Expression transformation to identify and filter out duplicate records based on specific criteria.

Q11. Differentiate between Joiner and Lookup transformations?
Ans:

  • Joiner Transformation: Combines data from two or more sources based on a join condition. It performs join operations similar to SQL joins (e.g., inner join, outer join) and outputs a single result set containing columns from all joined sources.
  • Lookup Transformation: Retrieves data from a relational table or flat file based on a lookup condition. It searches for matching records in the lookup source and returns corresponding values to the mapping.

Q12. How does Rank transformation handle string values?
Ans: The Rank transformation in Informatica assigns ranks to input rows based on specified sort criteria. When handling string values, the Rank transformation ranks strings based on their alphabetical order. For example, if the sort order is ascending, “A” would be ranked higher than “B,” and so on. If the sort order is descending, the ranking would be the opposite.

Q13. How do you differentiate stop and abort options in a workflow monitor?
Ans:

  • Stop: When you stop a workflow in the Workflow Monitor, it completes the current task and then stops the workflow gracefully. It allows the workflow to finish processing its current task before stopping.
  • Abort: When you abort a workflow, it immediately stops all tasks and terminates the workflow without completing the current task. It forcefully stops the workflow execution.

Q14. How to use PMCMD Utility Command?
Ans: The PMCMD utility command is used to interact with the Informatica server from the command line. Here’s how to use it:

  • To start a workflow: pmcmd startworkflow
  • To stop a workflow: pmcmd stopworkflow
  • To check workflow/session status: pmcmd getsessionstatistics

Q15. What is meant by Informatica PowerCenter Architecture?
Ans: The Informatica PowerCenter architecture consists of various components such as PowerCenter Clients, Repository Service, Integration Service, and Repository Database. These components work together to design,develop, and execute ETL processes. The architecture follows a client-server model where PowerCenter Clients, like Designer and Workflow Manager, are used for designing and managing ETL processes. The Repository Service manages metadata and provides access to the repository database where metadata is stored. The Integration Service executes workflows and sessions, fetching data from sources, transforming it, and loading it into targets.

Q16. How to use Normalizer Transformation in Informatica?
Ans: The Normalizer transformation in Informatica is used to normalize denormalized data, i.e., converting multiple rows of data into a single row with multiple columns. To use the Normalizer transformation:

  1. Drag and drop the Normalizer transformation onto the mapping designer.
  2. Connect the source qualifier to the Normalizer transformation.
  3. Define the structure of the normalized output in the Normalizer transformation.
  4. Map the source columns to the appropriate output columns in the Normalizer transformation.

Example: If you have data like Name1, Name2, Name3 in a single row, the Normalizer transformation can be used to convert it into three rows with a single Name column.

Q17. What are the output files created by the Informatica server at runtime?
Ans: The Informatica server generates several output files during runtime, including:

  • Session Log: Contains information about the session’s execution, including status, errors, warnings, and performance statistics.
  • Workflow Log: Contains information about the workflow’s execution, including the status of each task within the workflow.
  • Workflow Output File: Contains output generated by commands or scripts executed within the workflow.
  • Reject Files: Contains rows of data that were rejected during the session due to errors or transformation logic.
  • Session Detail File: Contains detailed statistics about the session’s performance, such as row counts, buffer statistics, and transformation timings.

Q18. Mention some use cases of Informatica?
Ans: Informatica is widely used for various data integration and data management tasks, including:

  • Data warehousing and business intelligence
  • Data migration and consolidation
  • Real-time data integration
  • Master data management
  • Data quality management
  • Cloud data integration
  • Big data integration and analytics

Q19. How do pre- and post-session shell commands function?
Ans: Pre-session shell commands and post-session shell commands in Informatica are used to execute custom shell scripts or commands before and after a session’s execution, respectively. These commands can be used for tasks such as setting up environment variables, performing cleanup operations, or triggering external processes.

Example: A pre-session shell command can be used to set up temporary directories or initialize logging, while a post-session shell command can be used to send notifications or archive session logs.

Q20. What is the difference between active and passive transformations in Informatica?
Ans:

  • Active Transformation: An active transformation in Informatica can change the number of rows that pass through it, such as Filter or Expression. It may alter the row’s structure or change the row’s content based on the transformation logic.
  • Passive Transformation: A passive transformation in Informatica does not change the number of rows that pass through it, such as Lookup or Sequence Generator. It maintains the row’s structure and does not alter the row’s content.

Q21. What is Dynamic Lookup Cache?
Ans: Dynamic Lookup Cache in Informatica is a feature that allows the Lookup transformation to be configured to use dynamic lookup cache. In dynamic cache mode, the cache is populated at runtime based on the data in the input pipeline, rather than being preloaded from a static source. This enables the lookup transformation to handle changing data without needing to reload the cache manually.

Q22. What is Informatica PowerCenter?
Ans: Informatica PowerCenter is a widely used enterprise data integration platform developed by Informatica Corporation. It provides a scalable, high-performance solution for designing, executing, and monitoring data integration workflows that extract, transform, and load data from various sources into target systems.

Q23. How do you perform data cleansing in Informatica?
Ans: Data cleansing in Informatica can be performed using various transformations and techniques such as:

  • Filter Transformation: Filtering out invalid or duplicate records based on specified criteria.
  • Expression Transformation: Using expressions to clean and standardize data values, such as removing whitespace or converting data types.
  • Lookup Transformation: Validating data against reference tables or lists to ensure data integrity.
  • Aggregator Transformation: Performing aggregation operations to identify and handle duplicate or inconsistent data.
  • Custom Transformation: Writing custom scripts or code to perform advanced data cleansing tasks.

Q24. Explain the concept of session partitioning in Informatica?
Ans: Session partitioning in Informatica involves dividing the data processing tasks within a session into multiple partitions that can be executed concurrently. This allows for parallel processing of data, improving performance by leveraging multiple CPU cores or nodes. Session partitioning can be based on various factors such as source data, target data, or transformation logic.

Q25. What are the features of Informatica Developer 9.1.0?
Ans: Some features of Informatica Developer 9.1.0 include:

  • Improved support for big data integration and processing
  • Enhanced data quality and profiling capabilities
  • Advanced mapping and transformation functionalities
  • Integration with cloud-based data sources and applications
  • Enhanced collaboration and version control capabilities

Q26. How can you schedule and automate Informatica workflows?
Ans: Workflows in Informatica can be scheduled and automated using the Workflow Manager or the Informatica Scheduler. You can define scheduling properties for workflows, such as recurrence, start time, and dependencies, to automatically trigger workflow executions at specified intervals or in response to events.

Q27. How can we filter rows in Informatica?
Ans: Rows can be filtered in Informatica using the Filter transformation. The Filter transformation evaluates each incoming row against a specified condition and passes only those rows that satisfy the condition to the next transformation in the mapping. The condition can be based on one or more columns in the row.

Q28. Describe the role of the Workflow Monitor in Informatica?
Ans: The Workflow Monitor in Informatica is used to monitor and manage the execution of workflows and sessions. It provides real-time visibility into the status of workflows, including information about running sessions, completed sessions, and any errors or warnings encountered during execution. The Workflow Monitor allows administrators to start, stop, and monitor workflow executions, as well as view detailed logs and performance statistics.

Q29. What can we do to improve the performance of Informatica Aggregator transformation?
Ans: To improve the performance of the Aggregator transformation in Informatica, you can:

  • Use sorted input data whenever possible to reduce the need for sorting within the transformation.
  • Limit the number of distinct groups by optimizing the grouping conditions.
  • Use incremental aggregation to update aggregate values incrementally rather than recalculating them for the entire dataset.
  • Increase the transformation’s cache size to accommodate more data in memory, reducing disk I/O operations.

Q30. What is the use of the Mapping Parameter and Mapping Variable in Informatica?
Ans:

  • Mapping Parameter: Mapping parameters in Informatica are placeholders for values that can be passed to a mapping at runtime. They allow for dynamic configuration of mappings without modifying their design. Parameters can be used to define connection information, file paths, or other configurable properties.
  • Mapping Variable: Mapping variables in Informatica are used to hold values that can be modified during the execution of a mapping. They are typically used for tasks such as capturing row counts, maintaining session state, or performing conditional logic within mappings. Variables can be updated using expressions, assignment statements, or through the use of predefined system variables.

Q31. How to use Pushdown Optimization in Informatica?
Ans: Pushdown Optimization in Informatica is a feature that allows the transformation logic to be pushed down to the database whenever possible, thus leveraging the processing power of the underlying database engine. To use Pushdown Optimization:

  1. Configure the session properties to enable Pushdown Optimization.
  2. Design mappings using supported transformations and SQL queries that can be pushed down to the database.
  3. Informatica determines which parts of the transformation logic can be pushed down based on its capabilities and the capabilities of the database.

Q32. What is the Surrogate Key?
Ans: A surrogate key is a unique identifier assigned to each record in a table to serve as a primary key. Unlike natural keys, which are based on the actual data attributes of the record, surrogate keys are system-generated and have no business meaning. Surrogate keys are commonly used in data warehousing and ETL processes to ensure data integrity and facilitate efficient data processing.

Q33. How does Informatica handle data integration challenges in a multi-cloud environment?
Ans: Informatica provides several features and capabilities to address data integration challenges in a multi-cloud environment, including:

  • Cloud Data Integration: Informatica offers cloud-based integration services that support connectivity to various cloud applications and platforms, allowing organizations to seamlessly integrate data across multiple cloud environments.
  • Data Quality and Governance: Informatica provides tools for data quality management and governance, enabling organizations to ensure data accuracy, consistency, and compliance across disparate cloud systems.
  • Scalability and Performance: Informatica’s scalable architecture and optimized processing engines enable efficient data integration and processing across distributed cloud environments, ensuring high performance and reliability.
  • Hybrid Integration: Informatica supports hybrid integration scenarios, allowing organizations to integrate data between on-premises systems and multiple cloud environments seamlessly.

Q34. Differentiate between Informatica and DataStage?
Ans:

  • Informatica: Informatica is an enterprise data integration platform that provides solutions for data integration, data quality, data governance, and master data management. It offers a range of products and services, including Informatica PowerCenter, Informatica Cloud, and Informatica Data Quality, designed to address various data integration and management needs.
  • DataStage: DataStage is an ETL (Extract, Transform, Load) tool developed by IBM as part of the IBM Information Server platform. It is used for designing, developing, and deploying data integration processes that extract data from various sources, transform it, and load it into target systems. DataStage offers parallel processing capabilities and supports connectivity to a wide range of data sources and targets.

Q35. What is OLAP?
Ans: OLAP (Online Analytical Processing) is a technology used for performing multidimensional analysis of data. It allows users to analyze and interact with data from different perspectives, such as time, geography, product, or customer, by aggregating and summarizing data across multiple dimensions. OLAP enables users to generate complex queries and reports, perform trend analysis, and make data-driven decisions based on multidimensional data analysis.

Q36. Explain the process of creating a reusable transformation in Informatica?
Ans: To create a reusable transformation in Informatica:

  1. Open the Informatica Designer and create a new transformation.
  2. Design the transformation logic using the required transformation objects and mappings.
  3. Once the transformation is designed, save it as a reusable transformation by right-clicking on the transformation object and selecting “Save As Reusable.”
  4. Specify a name and location for the reusable transformation, and save it to the repository.
  5. The reusable transformation can now be reused in multiple mappings by adding it to the mapping from the repository.

Q37. How do you implement error handling in Informatica workflows?
Ans: Error handling in Informatica workflows can be implemented using the following techniques:

  • Conditional Workflows: Use conditional links and decision tasks to route workflow execution based on the success or failure of preceding tasks.
  • Workflow Variables: Define workflow variables to capture error codes or status information from tasks, and use them to control workflow execution or trigger error-handling logic.
  • Email Notifications: Configure email tasks to send notifications to administrators or stakeholders in case of workflow failures.
  • Reusable Error Workflows: Design reusable error-handling workflows that can be called from main workflows to handle specific error scenarios.

Q38. Which is the T/R that builds only single cache memory?
Ans: The Lookup transformation builds only a single cache memory by default. However, the cache can be partitioned if partitioning is enabled for the Lookup transformation.

Q39. How does the Router transformation differ from the Filter transformation?
Ans:

  • Router Transformation: The Router transformation in Informatica routes data rows to multiple output groups based on specified conditions. Each output group represents a different condition or set of conditions, and each row is directed to one or more output groups based on the evaluation of these conditions.
  • Filter Transformation: The Filter transformation in Informatica filters rows of data based on a specified condition, passing only those rows that satisfy the condition to the next transformation in the mapping. Rows that do not meet the condition are discarded.

Q40. What are the Limitations of Pushdown Optimization?
Ans: Some limitations of Pushdown Optimization in Informatica include:

  • Limited support for complex transformations and custom functions that cannot be pushed down to the database.
  • Dependency on database capabilities and SQL dialects, which may vary across different database platforms.
  • Potential performance overhead due to increased network traffic and data movement between the database and the Informatica server.
  • Complexity in debugging and troubleshooting pushdown optimization issues, especially in heterogeneous environments with multiple database platforms.

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