Informatica Interview Questions and Answers- Part 5

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Informatica Interview Questions and Answers- Part 5
Landing a job at a top tech company or enterprise firm often means facing a rigorous interview process. When it comes to roles in data engineering, ETL development, or analytics, Informatica is still one of the most widely used platforms. Whether you’re interviewing at a large bank, healthcare company, or even a FAANG-level tech firm, knowledge of Informatica can be a key differentiator.

In this guide, we’ve gathered high-quality Informatica interview questions and answers to help you prepare for challenging technical rounds.  These questions are designed to help you think critically, structure your responses using real-world examples, and show that you can handle complex data environments. Whether you’re prepping for a panel interview or a technical round, let this guide be your go-to resource.

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Parameter files allow you to store and manage values for session parameters and variables outside the mapping, providing flexibility in configuration.

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Rejected data can be captured and stored in a separate target table using error logging and error handling techniques.

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A surrogate key simplifies data storage, improves performance, and maintains data integrity.

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Parallel processing can be achieved through session-level and mapping-level settings, as well as partitioning and pipeline partitioning.

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Null values can be handled using functions like ISNULL(), NVL(), COALESCE(), and using transformation properties like “Null Value Treatment.”

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Partitioning, hash auto-keys, and pushdown optimization can be used to optimize joins involving large data sets.

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The repository server primarily ensures the reliability and consistency of the repository, while the powerhouse server handles the execution of various procedures involving the elements within the server’s database repository.

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Using Informatica as an ETL (Extract, Transform, Load) tool over Teradata offers several benefits that make it a popular choice for data integration and management. Here are some advantages of using Informatica:

  • Broad Data Source and Target Support: Informatica supports a wide range of data sources and targets, including various databases, cloud-based services, applications, and flat files. This flexibility allows you to integrate and transform data from diverse systems.
  • Ease of Use and User-Friendly Interface: Informatica provides a user-friendly visual interface for designing ETL workflows. This makes it easier for both technical and non-technical users to design, configure, and manage data integration processes.
  • Codeless Development: Informatica’s visual design approach reduces the need for writing extensive code. This leads to faster development and easier maintenance of ETL processes.
  • Reusability and Modular Design: Informatica allows you to create reusable components, such as mappings and transformations. This promotes a modular design approach, saving time and effort when building complex ETL workflows.
  • Data Quality and Cleansing: Informatica offers built-in data quality and cleansing capabilities. You can validate, cleanse, and enrich data as it moves through the ETL pipeline, ensuring the accuracy and reliability of the integrated data.

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The main difference between “stop” and “abort” in a workflow monitor lies in the intention and consequences:

  • “Stop” implies a temporary pause with the intention to resume the workflow later. The workflow’s state is maintained, and it can continue from where it was paused.
  • “Abort” implies an immediate termination with the intention of not continuing the workflow. The workflow’s state might not be preserved, and any ongoing tasks might not be fully completed.

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A persistent lookup cache refers to a caching mechanism used in the Informatica PowerCenter ETL (Extract, Transform, Load) tool. The persistent lookup cache is a type of cache that stores lookup data in a file on disk, allowing the cached data to persist across sessions and potentially improving performance during data transformation processes.

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To set up a persistent lookup cache in Informatica, you need to follow these steps:

  • Create Lookup Transformation: Add a lookup transformation to your mapping within Informatica.
  • Configure Lookup: Configure the lookup transformation by specifying the source, target, and the lookup condition.
  • Choose Cache Type: In the lookup transformation properties, select the “Persistent Cache” option as the cache type.
  • Specify Cache Directory: Specify a directory where the persistent cache files will be stored on disk.
  • Define Cache Key: Define the cache key columns, which determine how the lookup data is cached and retrieved.
  • Enable Lookup Caching: Enable the caching option for the lookup transformation.

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PowerCenter on Grid refers to a high-performance and scalable data integration platform offered by Informatica, a leading company in data management and integration solutions. PowerCenter is designed to enable organizations to extract, transform, and load (ETL) data from various sources into a target data warehouse, data lake, or other destination.

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In Informatica, SUBSTR is a function used to extract a substring from a given string. It allows you to specify the starting position and the length of the substring you want to extract.

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To load the first and last records into a target table using Informatica, you can follow these general steps:

  • Define your source(s) that contains the data you want to load. Ensure that the source contains a column that you can use to determine the order of records.
  • Use a Sorter transformation after the source to sort the data based on the column that determines the order of records
  • Add two Expression transformations after the Sorter transformation:
  • In the first Expression transformation, create an output port that generates a constant value of 1 for the first record and 0 for the rest.
  • In the second Expression transformation, create an output port that generates a constant value of 1 for the last record and 0 for the rest.
  • Use two Filter transformations to filter out records with the constant value of 0 from the previous step. One Filter transformation will filter for the first record, and the other for the last record.
  • Define your target table in Informatica where you want to load the first and last records.
  • Create two separate target instances for loading the first and last records. Connect the respective Filter transformations to their respective target instances.
  • Create a workflow that links these transformations in the order mentioned above.
  • Connect the source to the Sorter transformation, then connect the Sorter transformation to the Expression transformations, and finally connect the Expression transformations to the Filter transformations and the target instances.
  • Configure the session properties, such as the database connection and the target load type.
  • Execute the workflow to run the session. The first and last records will be filtered, sorted, and then loaded into the target table.

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While all three terms involve data storage and management, they serve different purposes within the realm of data management and analysis. Data warehouses are large repositories for historical data, databases are used for transactional and operational systems, and data marts are specialized subsets of data warehouses for specific business areas.

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Informatica 8.0 introduced the concept of PowerExchange as a major feature enhancement over Informatica 7.0. PowerExchange is a module within Informatica’s suite of data integration tools that facilitates real-time data integration and extraction from various sources. It allows organizations to capture changes in source systems in real time and integrate that data into their data warehousing or analytics systems.

In Informatica 7.0, real-time data integration capabilities were more limited, and it primarily relied on traditional batch processing methods. Informatica 8.0’s PowerExchange changed this by providing a more efficient and robust way to capture and integrate real-time data updates.

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Expression Transformation is a concept related to ETL (Extract, Transform, Load) processes. It is a type of transformation used in Informatica PowerCenter, which is one of Informatica’s main products. It allows you to perform data transformations and calculations on incoming data rows before they are loaded into the target database or system.

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A filter transformation is a type of transformation used in data integration processes. It allows you to filter rows of data based on specified conditions, letting you include or exclude certain records from further processing in your data flow. The filter transformation acts as a data filter, and it is used to implement conditional logic on the data being processed.

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A “Full Outer Join” is a type of join operation in a relational database that combines rows from two or more tables based on a common column between them. Unlike inner joins, which only return matching rows, a full outer join returns all rows from both tables, filling in missing values with NULL where there are no matches. This type of join ensures that you get a complete set of data from both tables, including unmatched rows.

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A dimensional table, in the context of Informatica and data warehousing, refers to a type of table used in a dimensional modeling approach. Dimension tables are majorly used to describe dimensions. They consist of attributes that describe fact records in the table.