IBM ETL Informatica Developer Interview Questions and Answers

Drop command deletes the table structure along with data, while truncate command deletes only the data.

• Drop command removes the table structure and all its data, while truncate command only removes the data but keeps the table structure intact.

• Drop command is a DDL (Data Definition Language) command, while truncate command is a DML (Data Manipulation Language) command.

• Drop command is slower than truncate command as it ...read more

In my project, I perform validations such as data completeness, data accuracy, data transformation, and data integrity checks.

• Data completeness checks to ensure all required fields are populated

• Data accuracy checks to verify the correctness of data

• Data transformation checks to validate the transformation rules are applied correctly

• Data integrity checks to maintain data consistency and reliability

Star Schema is a type of database schema where a central fact table is connected to multiple dimension tables. Snowflake Schema is a normalized form of Star Schema where dimension tables are further normalized into sub-dimension tables.

• Star Schema has a central fact table connected to multiple dimension tables

• Snowflake Schema is a normalized form of Star Schema with sub-dimension tables

• Star Schema is denormalized for b...read more

Fact tables contain quantitative data while dimension tables contain descriptive attributes.

• Fact tables store measurable, quantitative data such as sales revenue or quantity sold

• Dimension tables store descriptive attributes related to the data in the fact table, such as product name or customer details

• Fact tables are typically normalized while dimension tables are denormalized for easier querying

• Example: In a sales dat...read more

Project architecture refers to the overall structure of a software project, including components, modules, and their interactions.

• Project architecture defines how different components of a software project are organized and interact with each other.

• It includes the high-level design of the system, such as the database structure, data flow, and integration points.

• Project architecture also involves decisions on technologi...read more

Challenges in ETL testing include data quality issues, complex transformations, and handling large volumes of data.

• Data quality issues such as missing or incorrect data can impact testing results

• Complex transformations can be difficult to validate and may lead to errors in the ETL process

• Handling large volumes of data can slow down testing processes and require efficient testing strategies

Roles and responsibilities refer to the tasks and duties assigned to an individual within a specific job or position.

• Roles define the position or job title of an individual within an organization.

• Responsibilities outline the specific tasks, duties, and expectations associated with that role.

• Roles and responsibilities help clarify expectations, define boundaries, and ensure accountability.

• Examples include testing data e...read more

Use a dictionary to find duplicate values in an array of strings in Python.

• Create an empty dictionary to store the count of each string in the array.

• Iterate through the array and for each string, check if it exists in the dictionary. If it does, increment the count. If not, add it to the dictionary with a count of 1.

• After iterating through the array, check the dictionary for any strings with a count greater than 1. The

Entry criteria and exit criteria are conditions that must be met before and after testing, respectively.

• Entry criteria are the conditions that must be fulfilled before testing can begin

• Exit criteria are the conditions that must be fulfilled before testing can be considered complete

• Entry criteria can include having test data ready, test environment set up, and test cases prepared

• Exit criteria can include all test cases ...read more

I am an experienced ETL Developer with a strong background in data integration and transformation.

• Over 5 years of experience in ETL development

• Proficient in using tools like Informatica, Talend, and SSIS

• Strong SQL skills for data querying and manipulation

• Experience in designing and implementing data warehouses

• Familiar with data modeling and ETL best practices

Active transformations change the number of rows that pass through them, while passive transformations do not change the number of rows.

• Active transformations can filter, update, or modify the number of rows in a data stream (e.g. Filter, Router, Update Strategy).

• Passive transformations do not change the number of rows in a data stream, they only allow data to pass through unchanged (e.g. Expression, Lookup, Sequence G

Different types of lookup caches include persistent cache, dynamic cache, and shared cache.

• Persistent cache stores lookup data in a flat file or database table for reuse across sessions.

• Dynamic cache dynamically loads lookup data into memory as needed during a session.

• Shared cache allows multiple sessions to share the same cache for improved performance.

Filter is used to pass or reject data based on conditions, while router is used to route data to different targets based on conditions.

• Filter is used to include or exclude data based on specified conditions.

• Router is used to direct data to different targets based on specified conditions.

• Filter is typically used to remove unwanted data, while router is used to split data flow.

• Example: Using filter to exclude records wit...read more

Truncate, delete, and drop are SQL commands used to remove data from a database, but they differ in their functionality.

• Truncate is a DDL command that removes all rows from a table, but keeps the structure intact.

• Delete is a DML command that removes specific rows from a table based on a condition.

• Drop is a DDL command that removes an entire table from the database, including its structure and data.

• Truncate is faster th...read more

Fact table, dimension table, star schema, and snowflake schema are concepts in data warehousing.

• Fact table contains the measures or metrics of the data warehouse.

• Dimension table contains the attributes or dimensions of the data warehouse.

• Star schema is a type of schema where a fact table is connected to multiple dimension tables.

• Snowflake schema is a type of schema where dimension tables are normalized into multiple re...read more

Mapping design is the process of creating a mapping between source and target data structures.

• It involves identifying the source and target data structures

• Defining the transformation rules to convert the source data to target data

• Creating a mapping document to document the mapping rules

• Examples include ETL mapping design for data integration projects