80+ ETL Developer Interview Questions and Answers

Difference between truncate, delete and drop

• Truncate removes all data from a table but keeps the structure

• Delete removes specific rows from a table

• Drop removes the entire table and its structure

• Truncate and drop cannot be rolled back, but delete can be

• Truncate is faster than delete as it does not log individual row deletions

Design a data structure for an ETL process to manage and transform data efficiently.

• Use a staging area (e.g., a temporary database) to hold raw data before transformation.

• Implement a schema for the target database that reflects the final data model.

• Utilize ETL tools like Apache NiFi or Talend for data extraction and loading.

• Incorporate logging and error handling mechanisms to track data quality.

• Consider using a data warehouse for analytical queries post-transformation.

Records in a table can be deleted using SQL DELETE statement.

• Use the DELETE statement followed by the table name to delete records.

• You can add a WHERE clause to specify which records to delete.

• Make sure to commit the transaction after deleting records to make the changes permanent.

Filter stage is used in ETL processes to selectively pass or reject data based on specified criteria.

• Filter stage helps in removing unwanted data from the input dataset.

• It can be used to apply conditions like filtering out duplicate records, selecting specific columns, or excluding certain values.

• For example, a filter stage can be used to only pass records where the sales amount is greater than $1000.

Star schema has a centralized fact table with denormalized dimensions, while snowflake schema has normalized dimensions.

• Star schema is simpler and easier to understand compared to snowflake schema.

• In star schema, dimensions are denormalized and directly connected to the fact table.

• Snowflake schema has normalized dimensions, meaning dimensions are further broken down into sub-dimensions.

• Star schema is more suitable for data warehousing and reporting purposes.

• Snowflake schema i...read more

Use a script to find and delete files older than 30 days.

• Use a script to identify files older than 30 days based on their creation or modification date

• Utilize a command like find or PowerShell to search for files older than 30 days

• Combine the script with a delete command to remove the identified files

Looking for new challenges and growth opportunities in a dynamic environment.

• Seeking opportunities to work on more complex ETL projects

• Interested in learning new technologies and tools in the field

• Want to work in a collaborative team environment to enhance skills and knowledge

Yes, I created a complex SSIS package to automate data migration between multiple databases.

• Used multiple data flow tasks to extract, transform, and load data

• Implemented error handling and logging to track data flow

• Utilized variables and expressions for dynamic behavior

• Included conditional logic for different data processing scenarios

I am using Talend version 7.3.1 for ETL development.

• I am currently using Talend version 7.3.1 for ETL development.

• The specific version of Talend being used is 7.3.1.

• Talend 7.3.1 offers various features and improvements for ETL processes.

Connected lookup is used in mapping to return multiple columns, while unconnected lookup is used in expressions to return a single value.

• Connected lookup is used in mapping to return multiple columns from a source, while unconnected lookup is used in expressions to return a single value.

• Connected lookup is connected directly to the source in the mapping, while unconnected lookup is called from an expression transformation.

• Connected lookup is faster as it caches the data, whil...read more

To create a parallel job, use parallel processing techniques to divide tasks into smaller subtasks that can be executed simultaneously.

• Identify tasks that can be executed independently and in parallel

• Use parallel processing techniques such as multi-threading or distributed computing

• Implement parallel job using ETL tools like Informatica or Talend

• Monitor and optimize parallel job performance to ensure efficient execution

Sort stage is used in ETL processes to sort data based on specified criteria before loading it into the target system.

• Sort stage helps in arranging data in a specific order for better analysis and reporting

• It can be used to remove duplicates from data before loading

• Sorting can be done based on multiple columns or expressions

• Example: Sorting customer data based on their purchase amount before loading into a data warehouse

Use TRY...CATCH block in SQL to handle errors in Stored Procedures

• Enclose the code inside a TRY block

• Use a CATCH block to handle any errors that occur

• Use RAISEERROR or THROW statement to raise custom error messages

• Use @@ERROR or @@ROWCOUNT to check for errors or affected rows

ETL is Extract, Transform, Load where data is extracted, transformed, and loaded into a data warehouse. ELT is Extract, Load, Transform where data is extracted, loaded into a data warehouse, and then transformed.

• ETL involves extracting data from source systems, transforming it according to business rules, and loading it into a data warehouse.

• ELT involves extracting data from source systems, loading it into a data warehouse, and then transforming it as needed.

• ETL is suitable f...read more

Linux permissions include read, write, execute, setuid, and setgid.

• Read permission allows a user to view the contents of a file or directory.

• Write permission allows a user to modify the contents of a file or directory.

• Execute permission allows a user to run a file or access the contents of a directory.

• Setuid permission allows a user to execute a file with the permissions of the file's owner.

• Setgid permission allows a user to execute a file with the permissions of the file's g...read more

ETL stands for Extract, Transform, Load while ELT stands for Extract, Load, Transform.

• ETL involves extracting data from source systems, transforming it, and then loading it into the target system.

• ELT involves extracting data from source systems, loading it into the target system, and then transforming it as needed.

• ETL is suitable for scenarios where data needs to be transformed before loading, while ELT is useful when raw data needs to be loaded first and then transformed.

• ETL...read more

Use a stored procedure to prompt user for input in ETL process.

• Create a stored procedure with parameters to accept user input.

• Use the parameters in the stored procedure to filter or manipulate data.

• Prompt the user for input values when calling the stored procedure.

Scope_identity() returns the last identity value in the current scope, while @@Identity returns the last identity value in the session.

• Scope_identity() is limited to the current scope, ensuring it only retrieves the identity value from the last insert in the same context.

• Example: If you insert a row in a stored procedure, Scope_identity() will return that row's identity value.

• @@Identity returns the last identity value generated for any table in the current session, regardless...read more

Initial load in ETL refers to the process of loading data from source systems into the data warehouse for the first time.

• Initial load is typically a one-time process to populate the data warehouse with historical data.

• It involves extracting data from source systems, transforming it as needed, and loading it into the data warehouse.

• Initial load is often done using bulk loading techniques to efficiently transfer large volumes of data.

• It is important to carefully plan and execut...read more

For Each Loop Container is used to iterate through a set of objects or files in SSIS packages.

• Used to perform repetitive tasks on a set of objects or files

• Can be used to loop through files in a directory or rows in a table

• Requires a data source and a variable to store the current object or file

• Can be configured to run in parallel or sequentially

SCD Type 1 in IICS involves overwriting existing data with new data without maintaining historical changes.

• In IICS, use the Mapping Designer to create a mapping that loads data from source to target.

• Use a Lookup transformation to check if the record already exists in the target table.

• If the record exists, update the existing record with new data using an Update Strategy transformation.

• If the record does not exist, insert the new record into the target table.

• Ensure that the ma...read more

Mapping is the process of defining the relationship between source and target data in ETL.

• Mapping involves identifying the source and target data structures.

• It also involves defining the transformation rules to be applied to the source data.

• Mappings can be simple or complex depending on the complexity of the data.

• Mapping is a critical step in ETL as it determines the accuracy and completeness of the data in the target system.

Lookups are used to retrieve related data from another table based on a common key.

• Lookups are used in ETL processes to enrich data by adding additional information from a reference table.

• They are commonly used to replace codes with corresponding descriptions, such as replacing product IDs with product names.

• Lookups can be performed using SQL joins or lookup transformations in ETL tools like Informatica or Talend.

Rollback and commit are database transactions used to manage changes made to the database.

• Rollback is used to undo changes made in a transaction and restore the database to its previous state.

• Commit is used to save the changes made in a transaction permanently to the database.

• Rollback is typically used when an error occurs during a transaction, while commit is used when the transaction is successful.

• Example: If a transaction to transfer money from one account to another fails...read more

Use GROUP BY and HAVING clause to find duplicates in a table.

• Use GROUP BY to group rows with same values together

• Use HAVING COUNT(*) > 1 to filter out duplicates

• Example: SELECT column_name, COUNT(*) FROM table_name GROUP BY column_name HAVING COUNT(*) > 1;

Optimizing session involves tuning session settings, utilizing efficient data loading techniques, and minimizing resource usage.

• Tune session settings such as buffer size, commit interval, and block size for optimal performance

• Utilize efficient data loading techniques like bulk loading, incremental loading, and parallel processing

• Minimize resource usage by optimizing SQL queries, reducing unnecessary transformations, and utilizing caching mechanisms

Loads in Informatica refer to the process of moving data from source to target in a data warehouse.

• Loads involve extracting data from source systems

• Transforming the data as needed

• Loading the data into the target data warehouse or database

• Loads can be full, incremental, or delta depending on the requirements

• Example: Loading customer data from a CRM system into a data warehouse for analysis