ZS Technologies ETL Developer Interview Questions and Answers

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 wareh...read more

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

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;

ETL Developers are responsible for designing, developing, and maintaining ETL processes to extract, transform, and load data from various sources into a data warehouse.

• Designing ETL processes to extract data from multiple sources

• Transforming data to meet business requirements

• Loading data into a data warehouse or other target systems

• Developing and maintaining ETL jobs using tools like Informatica, Talend, or SSIS

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, inse...read more

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