ETL (Extract, Transform, Load) Developers play a crucial role in managing data workflows and ensuring data is correctly processed and moved between systems. Interviewers assess candidates on ETL tools, data transformation techniques, SQL skills, and problem-solving abilities. The following 25 ETL Developer Interview Questions and Answers will help you prepare effectively.
Q1. What is ETL and why is it important?
ETL stands for Extract, Transform, Load. It is the process of extracting data from different sources, transforming it into a suitable format, and loading it into a target database or data warehouse.
Q2. What ETL tools are you familiar with?
Common ETL tools include Informatica, Talend, Microsoft SSIS, IBM DataStage, and Apache Nifi.
Q3. Explain the difference between ETL and ELT.
ETL transforms data before loading into the target system, whereas ELT loads raw data first and then transforms it within the target system.
Q4. How do you handle incremental data loads?
By using techniques like timestamps, change data capture (CDC), or comparing source and target data to identify new or updated records.
Q5. What are lookup transformations in ETL?
Lookup transformations allow you to look up data in a table or flat file to enrich or validate data during the ETL process.
Q6. How do you ensure data quality in ETL processes?
Implement validation checks, error handling, data cleansing, and monitoring to maintain data accuracy and consistency.
Q7. What is a surrogate key and why is it used?
A surrogate key is a unique identifier for a record in a data warehouse, often used instead of natural keys to handle slowly changing dimensions.
Q8. Explain the concept of Slowly Changing Dimensions (SCD).
SCDs handle changes in dimension data over time, with different types like Type 1 (overwrite), Type 2 (add new record), and Type 3 (add new attribute).
Q9. What is data staging in ETL?
Data staging is a temporary storage area where data is held during extraction and transformation before loading into the target system.
Q10. How do you optimize ETL performance?
By minimizing data movement, using partitioning, indexing, parallel processing, and efficient SQL queries.
Q11. What is a data pipeline?
A data pipeline automates the flow of data from source to target through extraction, transformation, and loading stages.
Q12. How do you handle error handling in ETL workflows?
By implementing error logs, notifications, and retry mechanisms to handle and resolve data or process errors.
Q13. What are the common data transformation techniques?
Techniques include filtering, sorting, aggregating, joining, data cleansing, and data enrichment.
Q14. What is the role of metadata in ETL?
Metadata provides information about data origin, structure, and transformations, helping in management and auditing of ETL processes.
Q15. Describe the difference between full load and incremental load.
Full load involves loading the entire dataset each time, while incremental load only processes new or changed data.
Q16. How do you monitor ETL jobs?
Using monitoring tools, logs, alerts, and dashboards to track job status, performance, and errors.
Q17. Explain partitioning and its importance in ETL.
Partitioning divides large datasets into smaller parts to improve query performance and manageability.
Q18. What is the difference between a workflow and a session in ETL tools?
A workflow is a set of tasks or processes, while a session is the execution instance of a workflow or task.
Q19. How do you handle data from multiple sources with different formats?
By applying data parsing, standardization, and transformation techniques to harmonize data.
Q20. What is the importance of documentation in ETL development?
Documentation ensures clarity, maintainability, and knowledge transfer regarding ETL processes and logic.
Q21. How do you deal with duplicate records in ETL?
By implementing deduplication logic using keys or matching criteria during transformation.
Q22. What are common challenges in ETL development?
Handling large volumes of data, ensuring data quality, dealing with source system changes, and performance tuning.
Q23. Explain how you would design an ETL process for a new data source.
Analyze source data, map requirements, design extraction, transformation, and loading steps, then test and optimize.
Q24. What is the role of SQL in ETL?
SQL is used for data extraction, transformation, validation, and loading tasks within ETL workflows.
Q25. How do you ensure security in ETL processes?
By implementing access controls, encryption, masking sensitive data, and following compliance requirements.