10 Data Warehousing Informatica Interview Questions and Answers

Data Warehousing with Informatica is a critical skill in the realm of data management and business intelligence. Informatica provides robust tools for data integration, transformation, and loading, making it a preferred choice for organizations looking to streamline their data processes. Its ability to handle large volumes of data efficiently and integrate seamlessly with various data sources makes it indispensable for building and maintaining data warehouses.

This article offers a curated selection of interview questions designed to test your knowledge and proficiency in Informatica. By working through these questions, you will gain a deeper understanding of key concepts and practical applications, ensuring you are well-prepared to demonstrate your expertise in any technical interview setting.

Data Warehousing Informatica Interview Questions and Answers

1. What are the different types of transformations available in Informatica?

In Informatica, transformations modify, aggregate, and cleanse data as it moves from source to target. Key transformations include:

• Source Qualifier: Converts source data types to Informatica native types and performs filtering and joining.
• Expression: Used for row-wise manipulation, such as calculations and string operations.
• Aggregator: Performs aggregate calculations on groups of data.
• Filter: Filters rows based on a condition.
• Joiner: Joins data from different sources based on a common key.
• Lookup: Retrieves data from a relational table, view, or synonym.
• Sorter: Sorts data based on specified keys.
• Router: Routes data into multiple transformations based on conditions.
• Update Strategy: Determines how to handle updates to existing records in the target.
• Sequence Generator: Generates unique numeric values, often used for primary keys.
• Rank: Selects the top or bottom N records based on a condition.
• Union: Combines data from multiple sources into a single pipeline.
• Normalizer: Converts denormalized data into a normalized format.
• Stored Procedure: Executes a stored procedure in a relational database.

2. Write a mapping to load data from a flat file into a target table, ensuring that duplicate records are removed.

To load data from a flat file into a target table while removing duplicates, use:

• Source Definition: Define the flat file structure.
• Source Qualifier: Read data from the flat file.
• Sorter: Sort data based on key columns.
• Aggregator: Remove duplicates by grouping data based on key columns.
• Target Definition: Define the target table structure.
• Target Load: Load transformed data into the target table.

The Aggregator Transformation is key for eliminating duplicates.

3. How do you handle error logging in Informatica?

Error logging in Informatica involves:

• Session Log Files: Generated for each session run, containing execution details, including errors and warnings.
• Error Handling in Mappings: Use the Error Handling transformation to capture and log errors, redirecting problematic rows to a separate error table or file.
• Workflow Logs: Provide information about workflow execution, including errors, accessible through the Workflow Monitor.
• Custom Error Logging: Implement custom error logging using built-in functions and transformations, such as the Expression and Router transformations.
• Email Notifications: Configure email notifications for errors, sending alerts to administrators or support teams.

4. How would you optimize a slow-running Informatica mapping?

To optimize a slow-running Informatica mapping:

• Source and Target Optimization: Ensure databases are optimized with indexing and partitioning, and use efficient SQL queries.
• Mapping Design: Simplify complex mappings, use reusable transformations, and minimize connected lookups.
• Transformation Optimization: Reduce active transformations, use early filters, and aggregate data at the source level.
• Session and Workflow Tuning: Adjust session properties like buffer size and commit intervals, and use bulk loading for large volumes.
• Partitioning: Parallelize data processing by partitioning at the source, target, or transformation level.
• Caching: Use caching for lookups and joiner transformations, ensuring appropriate cache size.
• Performance Monitoring: Continuously monitor performance using Informatica’s tools, identifying and addressing bottlenecks.

5. Write a mapping to perform incremental data loading using a date column.

For incremental data loading using a date column:

• Source Definition: Define the source table with a date column for identifying new or updated records.
• Mapping Design: Create a mapping with:
  • Source Qualifier: Read data from the source table.
  • Filter Transformation: Select records added or updated since the last load by comparing the date column with a stored value.
  • Target Definition: Define the target table for incremental data.
• Parameterization: Use mapping parameters or variables to store and update the last load date.
• Workflow Design: Create a workflow to execute the mapping and update the last load date parameter post-load.

6. Explain the difference between connected and unconnected lookups in Informatica.

Informatica lookups can be connected or unconnected:

Connected lookups are part of the data flow, receiving input directly from the pipeline and returning multiple columns. They are used for lookups on every row with smaller tables.

Unconnected lookups are not part of the data flow, called as a function using the :LKP expression, and return one column. They are efficient for conditional lookups or when the lookup table is large.

7. How do you implement slowly changing dimensions (SCD) Type 2 in Informatica?

To implement Slowly Changing Dimensions (SCD) Type 2 in Informatica:

• Source Definition: Define the source table for loading data into the dimension table.
• Target Definition: Define the target dimension table for storing historical data.
• Mapping: Create a mapping with:
  • Source Qualifier: To read data from the source.
  • Lookup Transformation: To check if the record exists in the target table.
  • Router Transformation: To route new and changed records.
  • Update Strategy Transformation: To specify the update strategy for the target table.
  • Sequence Generator: To generate unique surrogate keys for new records.
• Workflow: Create a workflow to execute the mapping and load data into the target table.

8. How would you configure and use a Sequence Generator transformation?

To configure and use a Sequence Generator transformation:

• Drag the Sequence Generator transformation to the mapping designer workspace.
• Configure properties:
  • Set the Start Value for the initial sequence value.
  • Set the Increment By value for the step size.
  • Optionally, set the End Value for the maximum sequence value.
  • Configure the Cycle option to restart the sequence after reaching the end value.
  • Set the Number of Cached Values for performance optimization.
• Connect the Sequence Generator to the target transformation where sequence values are needed.
• Map the NEXTVAL or CURRVAL ports to the appropriate fields in the target transformation.

9. Explain how to implement a dynamic lookup cache in Informatica.

To implement a dynamic lookup cache in Informatica:

• Configure the Lookup transformation to use a dynamic cache by selecting the “Dynamic Cache” option.
• Define the lookup condition and fields for cache updates, typically involving the primary key and other fields.
• Use the “NewLookupRow” port to determine if a row is an insert or update, with values of 0 for existing, 1 for insert, and 2 for update.
• Map the appropriate fields from the source to the Lookup transformation for correct cache updates.

10. What strategies do you use for performance tuning in Informatica?

Performance tuning in Informatica involves:

• Optimizing Source and Target Databases: Ensure databases are indexed and queries are optimized to reduce read and write times.
• Tuning Transformations: Minimize complex expressions and unnecessary transformations, using efficient built-in functions.
• Partitioning: Divide large datasets for parallel processing, improving performance.
• Caching: Use caching for lookup transformations to reduce database hits and speed up processing.
• Session and Workflow Tuning: Adjust properties like commit intervals, buffer sizes, and memory settings for optimization.
• Pushdown Optimization: Push transformation logic to the database level to reduce data movement and processing within Informatica.
• Monitoring and Profiling: Regularly monitor and profile workflows to identify bottlenecks and areas for improvement.