Informatica Interview Questions 2025

Informatica Interview Questions 2025

On May 1, 2025, Posted by , In Interview Questions, With Comments Off on Informatica Interview Questions 2025

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Preparing for an Informatica interview in 2025 can feel overwhelming, but with the right guidance, you can approach it with confidence. From questions on ETL processes to advanced features like dynamic partitioning and real-time data integration, hiring managers often test both your technical expertise and your problem-solving skills. I’ve seen many candidates struggle with scenario-based questions that require practical solutions to optimize ETL pipelines or resolve performance bottlenecks. If you’re aiming to stand out, mastering topics like Informatica PowerCenter, data transformations, workflow management, and troubleshooting is non-negotiable.

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That’s where this guide comes in. I’ve compiled a list of the most relevant and frequently asked Informatica interview questions, designed to help you anticipate what might come up in your next interview. With detailed answers and practical insights, this resource will sharpen your understanding of core concepts while preparing you for even the trickiest scenario-based challenges. Whether you’re a fresher or an experienced professional, this content will give you the edge you need to confidently ace your Informatica interview.

Beginner-Level Informatica Interview Questions

1. What is Informatica, and how is it used in data integration?

Informatica is a powerful data integration tool widely used for extracting, transforming, and loading (ETL) data from various sources to a target system. It acts as a bridge between disparate data sources, helping organizations unify and process their data efficiently. I often describe Informatica as the backbone of modern data warehousing because of its ability to handle large-scale data transformations and ensure data quality. Its user-friendly interface and drag-and-drop functionality make it accessible even to those who are new to ETL tools.

I use Informatica in my projects to create robust workflows that automate the process of data movement and transformation. It supports a variety of data formats, including relational databases, flat files, XML, and cloud systems. One of the standout features of Informatica is its error-handling capabilities, which allow me to identify and fix issues during the ETL process. This makes it an essential tool for building reliable and scalable data pipelines in complex systems.

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2. Explain the difference between a repository server and a repository database in Informatica.

The repository server and the repository database are two critical components of Informatica that work together to manage metadata. The repository server acts as the communication layer between Informatica clients and the repository database. In my experience, it ensures that all requests from users, such as retrieving or storing metadata, are processed correctly. The server also provides version control and multi-user access to enable collaborative development.

On the other hand, the repository database is where all the metadata is physically stored. This includes information about mappings, workflows, sessions, and other configurations I define in Informatica. Think of it as a library where every book (metadata) is cataloged. The repository server fetches these books when needed and ensures data consistency. Understanding this distinction is key when troubleshooting connection issues or optimizing the Informatica environment.

3. What are the key components of Informatica PowerCenter?

Informatica PowerCenter consists of several key components, each playing a unique role in the ETL process. The first component is the Repository Manager, which helps me manage the repository database. It allows me to organize, back up, and retrieve the metadata needed for building mappings and workflows. Without this, managing large-scale projects would be chaotic.

Next is the Designer, a tool I use to create mappings that define how data flows from source to target. It provides a visual interface where I can use transformations like aggregator, filter, or lookup to manipulate the data as required. The Workflow Manager is another critical component, allowing me to define the execution flow of mappings. I use it to schedule jobs, assign parameters, and link various tasks for seamless execution.

The final component I often interact with is the Monitor, which helps me track the execution status of workflows. It gives me insights into performance and error details, which are invaluable for debugging. Together, these components make Informatica PowerCenter a comprehensive solution for handling complex data integration projects.

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4. How does Informatica handle ETL (Extract, Transform, Load) processes?

Informatica handles the ETL process by dividing it into three distinct stages: Extraction, Transformation, and Loading. During the extraction phase, it connects to various sources, such as relational databases, flat files, or cloud storage, and pulls raw data. What I appreciate most is its ability to connect to multiple heterogeneous data sources simultaneously. This capability helps me consolidate diverse datasets into a unified pipeline.

The transformation stage is where Informatica truly shines. I can use a variety of transformations, like filter, aggregator, and expression, to clean, standardize, and enrich the extracted data. For example, I might use an expression transformation to calculate derived fields like profit margins. Here’s a simple example:

IIF(SALES > 1000, 'HIGH', 'LOW')

This logic categorizes sales data into “HIGH” or “LOW” based on a threshold, ensuring my data is consistent and meaningful. The final stage, loading, is where Informatica writes the processed data to the target system, such as a data warehouse or a flat file. It provides flexibility in terms of load strategies, supporting both incremental and bulk loading.

What I find especially useful is the error-handling mechanism in all three stages. If an error occurs during any phase, Informatica generates detailed logs that make troubleshooting easier. These features make it an indispensable tool for managing end-to-end data pipelines.

5. What is a mapping in Informatica?

A mapping in Informatica defines the flow of data from source to target, including any transformations applied in between. Think of it as a blueprint for the ETL process. When I create a mapping, I start by connecting the source definition, such as a table or file, to the target definition, like a database table. Between these, I add transformations to shape the data according to business requirements.

Mappings are highly flexible and allow me to incorporate complex business logic. For example, I can include joiner transformations to combine data from multiple sources or use filter transformations to exclude unnecessary records. In one of my projects, I used a router transformation to separate records based on region, ensuring that each dataset was sent to the appropriate team. Here’s a simple example of a mapping:

  • Source: Customer Data (CSV file)
  • Transformation: Filter Transformation (Excludes records with invalid email addresses)
  • Target: Validated Customer Data Table

The mapping ensures data integrity by removing invalid entries before loading. I also rely on mappings to automate repetitive tasks, saving time and reducing errors. By leveraging these capabilities, I can build efficient, reusable ETL pipelines tailored to specific business needs.

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6. Define a session in Informatica and its purpose.

A session in Informatica is a task used to execute a mapping. It bridges the gap between the design phase and execution, allowing me to run the ETL logic defined in the mapping. I use sessions to configure source and target connections, set parameters, and define error-handling rules. For instance, if I’m loading data from a flat file to a database, the session specifies how the file is read, processed, and written to the database.

In my experience, sessions are highly customizable. I can enable logging to monitor the ETL process, define session-level transformations, or even apply filters to fine-tune the data. The session also handles error recovery, making it possible to restart workflows from the point of failure, ensuring data consistency.

7. What is a workflow in Informatica, and how is it used?

A workflow in Informatica is a collection of tasks organized in a sequence to automate the ETL process. I use workflows to manage the execution flow, ensuring that dependencies between tasks are respected. For example, I often create workflows that first validate the source data, then transform it using a session, and finally load it into the target.

Workflows are incredibly flexible. Using the Workflow Manager, I can add decision tasks to control the flow based on conditions, or event wait tasks to pause execution until a specific trigger occurs. One of my workflows used a decision task to rerun a session if the source data size exceeded a threshold, ensuring efficient handling of large datasets.

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8. Explain the concept of a source qualifier transformation in Informatica.

The source qualifier transformation is automatically generated when I add a relational source to a mapping. It acts as a link between the source definition and Informatica, converting the raw source data into a format Informatica can process. I use the source qualifier to filter rows, join tables, or define custom SQL queries.

Here’s an example of a custom SQL query I used in a source qualifier:

SELECT customer_id, order_date, order_total FROM orders WHERE order_total > 500;

This query extracts only high-value orders, reducing unnecessary processing in the mapping. By filtering data early, I improve the overall performance of my ETL pipelines.

9. What is the purpose of the aggregator transformation?

The aggregator transformation is used to perform calculations like sum, average, or count on grouped data. It’s particularly useful when I need to generate summaries or roll-up data for reporting. For example, in one project, I used the aggregator to calculate the total sales per region.

Here’s a small example of an aggregator transformation:

  • Input: Transaction data with columns for region, product, and sales.
  • Aggregation Logic: Group by region and calculate the sum of sales.
  • Output: Total sales for each region.

Using the aggregator transformation simplifies complex calculations, ensuring accuracy and scalability in large datasets.

10. What are active and passive transformations in Informatica?

Informatica classifies transformations as either active or passive based on their impact on the row count. Active transformations can change the number of rows passing through them, such as a filter transformation that excludes unwanted data. In contrast, passive transformations do not alter the row count, like an expression transformation used for calculating derived values.

For example, a filter transformation that removes rows where sales < 100 is active because it reduces the dataset. On the other hand, an expression transformation to add a new calculated column is passive because the number of rows remains the same.

Understanding these distinctions helps me design efficient mappings tailored to the needs of each project.

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11. How does a lookup transformation work in Informatica?

A lookup transformation retrieves data from a reference table or file to enrich the source data during processing. I use it frequently for validation or to fetch additional details. For example, when processing transaction data, I might use a lookup transformation to retrieve customer names based on customer IDs.

Here’s an example of a lookup transformation logic:

  • Source: Transaction data with a customer_id column.
  • Lookup Table: Customer table with customer_id and customer_name.
  • Output: Enriched transaction data with customer names included.

By configuring the cache settings, I ensure that large lookups perform efficiently. This feature makes lookup transformations an essential part of most mappings.

12. What are the different types of caches used in lookup transformations?

Lookup transformations use two main types of caches: static cache and dynamic cache. A static cache is created when the session starts and remains unchanged throughout. I use this for scenarios where the lookup data doesn’t change during execution. In contrast, a dynamic cache updates itself as the session runs. This is particularly useful when processing data incrementally.

For instance, if I’m building a mapping to deduplicate customer records, I rely on a dynamic cache. This ensures that any new customer added during processing is immediately available for subsequent lookups. Configuring the right cache type improves performance and ensures data accuracy.

13. Explain the role of the router transformation.

The router transformation splits data into multiple groups based on conditions I define. Unlike the filter transformation, which allows only one condition, the router can create multiple output groups, making it more versatile. I often use it to separate data into categories, like regional sales or product types.

Here’s an example of a router transformation:

  • Input: Sales data with columns for region and sales amount.
  • Groups: North Region (region = ‘North’), High Sales (sales > 1000).
  • Output: Two separate datasets—one for the North region and another for high-value sales.

Using the router simplifies complex filtering logic, making mappings cleaner and easier to maintain.

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14. What is the difference between connected and unconnected lookups?

Connected lookups are part of the mapping data flow and return values directly to the pipeline. I use them when the lookup logic is integral to the mapping. Unconnected lookups, on the other hand, are called as functions and return a single value. These are useful for conditional lookups or when lookup logic needs to be reused.

Here’s an example of an unconnected lookup function:

:LKP.CUSTOMER_NAME (CUSTOMER_ID)

This function retrieves a customer name based on a provided ID. I use connected lookups for richer datasets and unconnected lookups for simpler, reusable tasks.

15. Define the term “parameter file” in Informatica.

A parameter file in Informatica stores runtime variables like source or target paths, database connections, and session parameters. I use it to make mappings and workflows more dynamic, eliminating the need to hard-code values. This flexibility makes managing different environments, like development and production, much easier.

For example, in a parameter file, I might define the database connection details:

[DEV_CONNECTION]
DBUSER=admin  
DBPASS=password  
DBNAME=SalesDB

When running a session, Informatica reads these values, ensuring that the mapping connects to the correct database without altering the workflow configuration. This feature enhances portability and scalability.

16. How do you handle performance tuning in Informatica mappings?

In my experience, performance tuning in Informatica mappings involves multiple strategies to improve the efficiency of data processing. First, I focus on optimizing the source and target connections by ensuring that the database queries are efficient and that the connection settings are appropriate for the volume of data. For example, when querying large tables, I use pushdown optimization to push transformation logic to the database, which significantly reduces the amount of data transferred.

Additionally, I carefully analyze transformations. I often choose the most efficient transformations based on the specific scenario. For instance, using a filter transformation early in the mapping to eliminate unnecessary rows reduces the workload for subsequent transformations. Partitioning and concurrent sessions are also great ways to improve performance by distributing the processing across multiple nodes. Lastly, I make sure to monitor session logs to identify and resolve bottlenecks, such as slow queries or insufficient memory usage.

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17. What are the various types of repositories in Informatica?

Informatica uses several types of repositories to store metadata and session information. The two main types of repositories are the PowerCenter Repository and the Repository Service. The PowerCenter Repository stores all the metadata related to mappings, workflows, sessions, and other design objects. It is the core repository that holds all the configuration and transformation logic.

The Repository Service manages repository objects and provides a way for multiple users to work concurrently on a project. This service is responsible for managing tasks such as version control and metadata access. Local repositories are smaller, user-specific repositories, and global repositories are shared repositories used across the entire organization. I typically use global repositories when working in teams, as they allow seamless sharing of objects like mappings, sessions, and workflows.

18. What is the difference between normal load and bulk load in Informatica?

Normal load and bulk load are two types of data loading methods used in Informatica. In a normal load, Informatica processes each row individually, performing all transformations and validations. While this method offers more flexibility, it can be slower, especially when working with large datasets. I often use this method when data quality checks, validations, or complex transformations are necessary.

On the other hand, bulk load is designed for faster data loading. It bypasses certain transformation steps to directly load large volumes of data. For example, in a bulk load, transformations like expression or aggregation might be skipped. I typically choose bulk load when performance is a priority and the data does not require complex processing. However, it’s important to ensure that the target database can handle the bulk load efficiently.

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19. Explain what reusable transformations are in Informatica.

A reusable transformation in Informatica allows me to define a transformation once and reuse it in multiple mappings. This is especially useful when the same logic is needed across different projects or processes, saving me time and effort. For example, if I frequently need to clean data by removing leading and trailing spaces, I can create a reusable expression transformation that performs this task.

Reusable transformations can be stored in a shared folder and included in any mapping that needs the transformation logic. By reusing transformations, I ensure consistency across projects and reduce the risk of errors. This also makes maintenance easier because any changes made to the reusable transformation automatically reflect in all mappings that use it.

20. What is the difference between joiner and lookup transformations?

Both joiner and lookup transformations are used to combine data from different sources, but they serve different purposes and are used in different scenarios. A joiner transformation is used when I need to join two sources based on a common key, like a SQL join. It supports different types of joins, such as inner, left, right, and full outer joins. I typically use a joiner when the sources are not related in the source qualifier but need to be merged during processing.

In contrast, a lookup transformation is used to retrieve a single value from a reference table or file based on a lookup condition. I often use lookups to enrich source data with additional attributes or validate records against a reference dataset. The primary difference is that a joiner works with multiple rows of data, while a lookup typically returns only a single matching value for each record.

21. What are session parameters, and how are they used?

Session parameters in Informatica are variables defined at the session level, allowing me to pass values into the session during execution. These parameters provide flexibility in managing mappings, as I can use them to change configurations like source and target paths, database connections, or transformation logic without altering the mapping itself. For example, I might use a session parameter to specify the file location for my input data.

Here’s an example of defining a session parameter:

$Source_File = /data/source_file.csv
$Target_Table = target_table_name

In the session, I can reference $Source_File and $Target_Table to make the mapping dynamic and easier to maintain. Using session parameters is crucial for handling different environments (development, testing, production) without hardcoding values into the mappings.

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22. Define the term “target load order” in Informatica.

The target load order refers to the sequence in which data is loaded into multiple target tables in a mapping. I can define the target load order when there are multiple targets to ensure that data is loaded in the correct order. For example, if I’m loading data into both a staging table and a final table, I might want to load the staging table first to ensure the data is validated and processed before being moved to the final destination.

By defining the target load order, I avoid data integrity issues such as loading dependent tables in the wrong sequence. In Informatica, I can specify the target load order at the session level using the Target Load Order property, allowing me to manage the flow of data more effectively.

23. What is the purpose of the rank transformation?

The rank transformation is used to assign a rank to each record in a dataset based on a specified sort order. This transformation is useful when I need to identify top N records or the lowest N records. For example, in a sales report, I can use the rank transformation to retrieve the top 10 highest-selling products.

Here’s a small example of using the rank transformation:

  • Input: A list of products with sales data.
  • Rank Logic: Rank products based on sales in descending order.
  • Output: Top 10 highest-selling products.

The rank transformation makes it easy to generate ranked datasets, which is useful for reporting and analytical purposes.

24. Explain the concept of incremental loading in Informatica.

Incremental loading refers to the process of loading only the data that has changed since the last load, rather than reloading the entire dataset. This approach is often used to improve performance and reduce the load on both source and target systems. In my experience, I often implement incremental loading by using a timestamp or high-water mark to track the last processed record.

Here’s an example of how incremental loading works:

  • Initial Load: Load all records from the source to the target.
  • Subsequent Loads: Load only records that have a modified timestamp greater than the last load date.

Incremental loading minimizes the volume of data processed and ensures that the ETL process remains efficient over time, particularly in large datasets.

25. What is the difference between static and dynamic cache?

The difference between static and dynamic cache in lookup transformations lies in how the cache is updated during the session. A static cache is built once at the beginning of the session and remains unchanged throughout. I typically use static cache when the lookup data does not change during processing. This is more efficient when the reference data is small and doesn’t require frequent updates.

On the other hand, a dynamic cache is updated as records are processed. This is useful in situations where new records may need to be added to the cache during the session. For example, if I’m processing a list of customer orders and need to add new customers to the cache dynamically, I use a dynamic cache. It ensures that new data is available for lookups without having to restart the session.

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26. How does Informatica handle error logging?

Informatica handles error logging using session logs, workflow logs, and error tables. For every ETL session, Informatica generates a session log that contains information about errors such as data type mismatches, constraint violations, or transformation failures. I find session logs helpful for pinpointing the exact stage where an error occurs. Additionally, rejected data can be directed to error tables for further analysis.

For example, if I configure a session to write rejected rows into an error file, it will capture information like this:

Error Code: 36401
Error Message: Data truncation for column 'Product_Name'
Rejected Row: ID:101, Product_Name: 'UltraLongProductNameExceedingLimit', Quantity: 5

This setup allows me to systematically review and address data issues while minimizing manual intervention during debugging.

27. What is the use of the expression transformation in Informatica?

The expression transformation in Informatica is used for performing row-level operations such as calculations, string manipulations, and conditional logic. I often use this transformation for tasks like normalizing data formats or computing new fields.

For example, suppose I want to create a full name by concatenating first and last names:

Full_Name = First_Name || ' ' || Last_Name

I define this formula in the expression transformation properties. Here’s a step-by-step snippet for applying a discount calculation:

Discounted_Price = IIF(Total_Price > 100, Total_Price * 0.90, Total_Price)

This logic applies a 10% discount if the price exceeds $100. Such calculations help me manipulate data directly in Informatica without additional SQL queries.

28. Explain what metadata is in Informatica.

In Informatica, metadata describes the structure, configuration, and properties of ETL objects such as mappings, transformations, and sessions. The Informatica repository stores metadata, which helps the ETL engine understand and execute workflows.

For instance, metadata for a table transformation might look like this:

Source Table: Orders
Columns: Order_ID, Customer_Name, Order_Date, Total_Amount
Target Table: Processed_Orders
Mapping: Total_Amount -> Total_Sales
Transformation: Order_Date -> To_Date(Order_Date, 'YYYY-MM-DD')

This metadata ensures that the ETL process knows how to read, transform, and write data. In my experience, accurate metadata documentation is key to debugging and enhancing workflows.

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29. What is a workflow monitor in Informatica, and why is it important?

The workflow monitor in Informatica is a real-time monitoring tool that shows the status of workflows and sessions. I use it to check whether tasks succeed, fail, or remain in progress. It also allows me to analyze session logs directly for quick troubleshooting.

For example, if a session fails, I can retrieve detailed error information like this:

Session Status: FAILED
Start Time: 2024-11-25 10:00 AM
Error: SQL Transformation - ORA-00942: Table or view does not exist

The monitor also provides options to restart failed workflows or recover from a specific checkpoint. This helps maintain workflow continuity without rerunning the entire process, saving both time and resources.

30. How do you define and use variables in Informatica mappings?

I use mapping variables in Informatica to store values that can dynamically change during the execution of ETL processes. These variables help with tasks like incremental data loading or handling conditional logic. I define variables in the Mapping Designer under the variable tab and reference them in expressions or transformations.

For instance, if I need to filter records based on a timestamp, I define a variable $$Last_Load_Date and use it in the filter transformation:

Filter Condition: Transaction_Date > $$Last_Load_Date

Here’s how I update the variable during each session:

Post-Session Variable Assignment: $$Last_Load_Date = MAX(Transaction_Date)

This ensures only new data is processed in subsequent runs, making the process efficient and adaptable to changing data scenarios.

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Advanced Informatica Interview Questions

31. What is the purpose of partitioning in Informatica, and how does it work?

Partitioning in Informatica improves ETL performance by dividing the data into smaller subsets and processing them concurrently. This enables the efficient utilization of system resources like CPU and memory. In my experience, partitioning is crucial for handling large datasets or meeting strict performance requirements.

In Informatica, I define partitions at the session level, choosing methods like key-based partitioning, round-robin, or hash-based partitioning based on the data characteristics. For instance, if I partition by a key such as “region,” each partition processes data for one region, ensuring balanced workloads. Here’s a configuration example:

Partition Type: Hash Auto-Keys
Key: Region
Number of Partitions: 4

This setup processes data for four regions simultaneously, significantly reducing the overall ETL execution time.

32. How can you implement SCD (Slowly Changing Dimension) Type 2 in Informatica?

To implement SCD Type 2 in Informatica, I create mappings that maintain historical records by adding new rows whenever changes occur. This ensures a complete audit trail of dimension changes over time.

Here’s a step-by-step example:

  1. Use a lookup transformation to compare source data with the target table.
  2. Apply an expression transformation to determine whether the record is new or updated.
  3. Route data using a router transformation:
    • Insert new records into the target table.
    • Update existing records by marking them as inactive and adding new rows.

A sample mapping might look like this:

Source -> Lookup (Target) -> Router
Router Outputs:
    New Records -> Insert into Target
    Updated Records -> Update Target with End_Date, Insert New Row

This approach ensures that historical data is preserved for all dimension changes.

33. Explain the differences between dynamic partitioning and static partitioning in Informatica.

Dynamic partitioning automatically determines the number of partitions at runtime based on the source data size or target configuration. In contrast, static partitioning requires me to predefine the number and type of partitions during session configuration.

For example:

  • Static Partitioning: I specify four partitions for processing, regardless of data size.
  • Dynamic Partitioning: Informatica adjusts the number of partitions based on available resources and data volume.

Dynamic partitioning offers greater flexibility and adaptability in real-world scenarios, while static partitioning provides more predictable performance when data size and system resources are constant.

34. How do you optimize a lookup transformation for better performance?

Optimizing a lookup transformation involves reducing the time spent fetching and processing reference data. In my experience, using appropriate cache settings and indexes significantly improves lookup performance.

Here are the key optimization techniques I use:

  • Enable caching: Use static or dynamic cache to store lookup data in memory.
  • Filter lookup data: Use SQL overrides to retrieve only necessary columns and rows.
  • Index optimization: Ensure the lookup table has proper indexes on the lookup key.

For example, using a SQL override in a lookup transformation:

SELECT ID, Name FROM Customers WHERE Active = 1

This ensures only active customer data is cached, reducing memory usage and improving performance.

35. What is pushdown optimization, and how is it implemented in Informatica?

Pushdown optimization allows me to push transformation logic to the database, reducing data movement and leveraging database processing power. In Informatica, I configure pushdown optimization at the session level to execute SQL logic directly in the source or target database.

For example, instead of processing a filter transformation in Informatica, I use pushdown optimization to execute this condition in the database:

SELECT * FROM Orders WHERE Order_Date > '2024-01-01'

To enable pushdown, I select the Pushdown Optimization option in the session properties and choose the level (source-side, target-side, or full). This minimizes data transfer and improves session performance.

36. Describe the concept of session recovery in Informatica.

Session recovery in Informatica ensures that a session can resume from the point of failure, reducing the need to reprocess the entire data. I often rely on this feature to maintain data consistency and save processing time, especially during large ETL operations.

When session recovery is enabled, Informatica creates recovery tables in the target database. These tables track the rows already processed, allowing the session to restart from the last checkpoint. For example, if a session writing to a target table fails halfway, it resumes from the last committed record instead of reprocessing the entire dataset. To enable session recovery, I ensure the session’s recovery property is turned on and configure appropriate commit intervals.

37. How can you implement parallel processing in Informatica workflows?

Parallel processing in Informatica workflows improves ETL performance by executing multiple tasks or sessions simultaneously. In my experience, I achieve this by configuring concurrent workflows or leveraging partitioning within mappings.

For example:

  • In a workflow, I schedule multiple sessions to run concurrently by enabling the Allow Concurrent Run property in the workflow manager.
  • At the mapping level, I use partitioning to process data subsets in parallel. For instance, if I’m processing a sales dataset, I can partition it by region (North, South, East, West) for simultaneous processing.

Here’s a basic workflow setup for parallel processing:

Workflow:
  - Task A (Data Extraction) -> Parallel Sessions:
       - Session 1 (Region North)
       - Session 2 (Region South)
       - Session 3 (Region East)
       - Session 4 (Region West)

This approach ensures better resource utilization and faster execution.

38. What is the difference between active and passive transformations, with examples?

Active transformations change the number of rows passing through them, while passive transformations maintain the same row count. This distinction is critical when designing mappings to ensure data accuracy.

  • Active Transformation Example: A Filter transformation removes rows that don’t meet a condition, reducing the row count.
Filter Condition: Sales > 1000

Rows with sales below 1000 are excluded, altering the row count.

  • Passive Transformation Example: An Expression transformation calculates a new field without affecting the number of rows.
Input: Price, Quantity
Expression: Total = Price * Quantity

This adds a new column but keeps the row count unchanged.

By combining both types of transformations strategically, I ensure optimal mapping performance and data accuracy.

39. Explain how to use parameterized connections in Informatica.

Parameterized connections allow me to dynamically configure source or target database connections at runtime. This feature is invaluable when dealing with multiple environments like development, testing, and production.

I create parameterized connections by defining connection variables in a parameter file. For example:

$DBConnection_Source=SourceDB_Prod
$DBConnection_Target=TargetDB_Test

In the session properties, I refer to these variables instead of hardcoding connection details. At runtime, Informatica uses the parameter file to determine the appropriate connections, ensuring flexibility and reducing the risk of manual errors.

40. How does Informatica support real-time data integration?

Informatica supports real-time data integration through features like web services, message queues, and CDC (Change Data Capture). In my projects, I use these features to ensure near-instantaneous data synchronization across systems.

  • Web Services: Informatica processes data in real-time by exposing or consuming web services. For instance, a web service hub allows Informatica to handle incoming requests dynamically.
  • Message Queues: Tools like JMS enable Informatica to read and write messages in real-time for seamless system integration.
  • CDC: By monitoring database logs, Informatica captures and processes only the changes, ensuring real-time updates without reprocessing the entire dataset.

Here’s a basic example of real-time CDC:

Source: Database logs
Process: Read updates (INSERT, UPDATE, DELETE)
Target: Update data warehouse

Real-time data integration is critical for applications like fraud detection and instant reporting, and Informatica makes it seamless to implement.

Scenario-Based Informatica Interview Questions

41. How would you design an Informatica mapping to deduplicate data in a source table?

To design a mapping to deduplicate data in a source table, I would use a Sorter transformation followed by an Aggregator transformation. The Sorter transformation helps sort the data based on key columns, while the Aggregator groups records and retains only unique ones.

In my experience, I configure the Sorter transformation to sort the input data and enable the Distinct Rows property. Afterward, I use the Aggregator transformation to group the data by the deduplication key (e.g., Customer ID) and pass only the first record in each group to the target. This ensures that only unique records are loaded into the target.

Source -> Sorter (Distinct Rows) -> Aggregator (Group by Customer ID) -> Target

This setup is effective for ensuring clean, deduplicated data without manual intervention.

42. Describe how you would handle a scenario where a workflow fails in the middle of execution.

When a workflow fails mid-execution, my first step is to identify the root cause by examining the session logs in the workflow monitor. Common issues include connection failures, transformation errors, or target constraints.

To handle such scenarios:

  • Enable session recovery: I configure the session to use recovery mode, allowing it to restart from the last commit point.
  • Error logging: I use Informatica’s error logs to track failed rows, isolate problematic data, and reprocess it.
  • Testing fixes: Before resuming the workflow, I validate fixes in a sandbox environment to prevent further failures.

By combining these practices, I ensure smooth recovery and minimal data reprocessing.

43. If you have multiple sources with different formats, how would you integrate the data using Informatica?

To integrate data from multiple sources with varying formats, I rely on Source Qualifier transformations and data standardization techniques. For instance, I connect the sources (e.g., flat files, databases, XMLs) to individual source definitions and then use transformations to harmonize the data.

Steps I follow:

  • Extract data: Read data from each source using Source Qualifiers.
  • Standardize format: Apply Expression transformations to standardize fields like date, numeric formats, or string cases.
  • Combine sources: Use Joiner or Union transformations to merge data into a single pipeline.
  • Load data: Send the standardized, integrated data to the target.

This approach ensures a seamless flow of data despite format differences.

44. How would you implement incremental loading for a source with millions of records?

For incremental loading, I would use a Change Data Capture (CDC) mechanism to process only new or updated records from the source. This method improves performance and reduces resource consumption.

Steps:

  • Add a timestamp column in the source table to track record changes.
  • Filter records: Use a Source Qualifier with a filter condition, such as:
WHERE Last_Updated_Timestamp > (SELECT MAX(Last_Load_Timestamp) FROM Target)
  • Load data: Load only filtered records into the target system.
  • Update tracking: After each load, update the Last_Load_Timestamp value in a control table.

This strategy ensures efficient handling of large data volumes.

45. Design a solution to handle changes in source data schema without impacting the target in Informatica.

To handle schema changes in the source without affecting the target, I follow a dynamic design approach using parameter files and dynamic mappings.

Steps:

  1. Parameterize source definitions: Use parameter files to define source field names, data types, and lengths dynamically.
  2. Dynamic schema propagation: Enable the schema propagation feature in mappings, which automatically adapts to source schema changes.
  3. Target mappings: Map source fields to generic target fields (e.g., Field1, Field2) to reduce dependency on source structure.
  4. Test schema updates: Before deploying, I validate new schema changes in a staging environment to ensure compatibility.

This design minimizes manual intervention and ensures robust data integration.

46. Explain how you would manage error handling for a mapping with multiple transformations.

In a mapping with multiple transformations, I manage error handling by incorporating error capture and logging mechanisms. This approach helps isolate errors and ensures data integrity without halting the workflow.

Steps I take:

  1. Configure session logging: I enable detailed session logs to capture row-level errors.
  2. Use error ports: In transformations like Lookup or Update Strategy, I use error ports to capture problematic data and redirect it to an error table.
  3. Create a reject file: Configure the session to generate a reject file that stores invalid rows for debugging.
  4. Add an error handling layer: I include an Expression transformation to generate custom error messages and route them to a dedicated error log table.

By implementing these practices, I ensure efficient error tracking and resolution without disrupting the main data flow.

47. If a session fails due to a connection issue, how would you ensure data consistency when restarting?

When a session fails due to a connection issue, my primary goal is to ensure data consistency by leveraging session recovery and checkpointing features in Informatica.

Steps I follow:

  1. Enable session recovery: I configure the session to restart from the last commit point, ensuring no duplicate or missed records.
  2. Use database transaction logs: For databases, I verify the target’s transaction logs to identify committed and uncommitted data.
  3. Reprocess failed data: For partially processed data, I design a recovery mapping that selectively processes only the uncommitted rows.

These strategies ensure that no data is lost or duplicated during recovery.

48. How would you optimize an Informatica workflow that processes large volumes of data?

To optimize workflows handling large datasets, I focus on improving performance and resource utilization through specific tuning techniques.

Key optimizations I use:

  • Partitioning: I split data processing across multiple partitions to enable parallel processing.
  • Pushdown optimization: I push transformations to the database to reduce Informatica’s workload.
  • Filter early: I use Source Qualifiers to filter unnecessary data before it enters the pipeline.
  • Optimize lookups: I use dynamic or static caching to reduce lookup overhead.

These optimizations ensure faster processing and efficient use of resources.

49. Create a mapping to identify and remove null values from the source data before loading it into the target.

To remove null values, I design a mapping that uses an Expression transformation to filter out null records before sending them to the target.

Example mapping logic:

  • Source Qualifier: Extract data from the source.
  • Expression Transformation: Add a condition to check for null values:
IIF(ISNULL(column_name), 'REJECT', 'ACCEPT') AS flag
  • Filter Transformation: Pass only records with the flag ‘ACCEPT’ to the target.
  • Load data: Send the cleaned data to the target table.

This mapping ensures that only valid, non-null records reach the target, maintaining data quality.

50. Explain how you would design a mapping to implement conditional logic for different data transformations.

To implement conditional logic, I use a Router transformation, which dynamically routes data based on specified conditions.

Steps I follow:

  • Source Qualifier: Extract data from the source.
  • Router Transformation: Define multiple groups for different conditions, such as:
Group1: column_value > 100
Group2: column_value <= 100
  • Transformation logic: Apply different transformation rules to each group.
  • Target tables: Load each group into the appropriate target table.

Using the Router transformation, I ensure that each data subset undergoes specific processing based on its condition, making the mapping flexible and efficient.

Conclusion

Gaining expertise in Informatica Interview Questions 2025 is a game-changer for anyone aiming to excel in the field of data integration and ETL. Informatica remains a cornerstone in managing complex data workflows, and having a strong command of its features positions you as a highly sought-after professional. From mastering mappings and transformations to implementing advanced optimizations like pushdown processing and session recovery, these questions equip you to tackle challenges with precision. By practicing scenario-based questions, you not only strengthen your problem-solving skills but also prepare yourself for real-world scenarios that demand innovation and expertise.

Preparing for Informatica Interview Questions gives you the confidence to stand out in any interview setting. Whether you’re just starting your career or are an experienced professional aiming for higher roles, this knowledge showcases your capability to handle diverse data needs. With businesses relying more on efficient data solutions, becoming proficient in Informatica ensures you’re not just ready for your next interview but also for a thriving career in the fast-evolving world of data integration.

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