ZS Technologies ETL Developer Interview Questions and Answers

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.

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.

Transform stage is used in ETL process to apply business rules, clean and enrich data before loading into target database.

• Transform stage is used to apply business rules to the data.

• It is used to clean and standardize data before loading into the target database.

• Transform stage can also be used to enrich data by combining multiple sources or adding calculated fields.

• Examples include converting data types, removing dupl

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 warehou

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 executio

SCD stands for Slowly Changing Dimension, a technique used in data warehousing to track changes in dimension attributes over time.

• SCD is used to maintain historical data in a data warehouse

• There are different types of SCDs - Type 1, Type 2, and Type 3

• Type 1 SCD overwrites old data with new data

• Type 2 SCD creates a new record for each change and maintains history

• Type 3 SCD keeps a limited history by adding columns to tr