ETL Testing Pertanyaan dan Jawaban Wawancara
Ques 11. What is data skew, and how does it impact ETL processing?
Data skew occurs when the distribution of data is uneven, leading to some processing nodes or partitions handling significantly more data than others. It can impact ETL processing by causing performance bottlenecks and resource contention.
Example:
In a parallel processing environment, data skew may result in certain nodes processing much larger volumes of data, slowing down the overall ETL process.
Ques 12. Explain the concept of data purging in ETL.
Data purging involves the removal of obsolete or unnecessary data from the target system to optimize storage and improve performance. It is essential for maintaining data quality and system efficiency.
Example:
In a data warehouse, data purging may involve deleting records that are no longer relevant or archiving historical data to a separate storage location.
Ques 13. What is the purpose of a surrogate key in ETL, and how is it different from a natural key?
A surrogate key is a system-generated unique identifier used in the target system to uniquely identify records. It is different from a natural key, which is a key derived from the actual data attributes of a record.
Example:
While a natural key might be a combination of name and birthdate, a surrogate key could be a sequentially generated number assigned to each record for simplicity and efficiency.
Ques 14. What are the advantages of using ETL testing automation tools?
ETL testing automation tools can improve efficiency, reduce manual errors, and accelerate the testing process. They offer features such as test case generation, data comparison, and result reporting.
Example:
Using an ETL testing automation tool, you can schedule and run tests automatically, ensuring consistent and repeatable testing processes.
Ques 15. How do you handle data quality issues in ETL testing?
Handling data quality issues in ETL testing involves identifying and addressing issues such as missing values, duplicate records, and inconsistencies. It may include data cleansing, validation rules, and error handling mechanisms.
Example:
If a source system contains missing values, ETL processes should be designed to handle them, either by replacing them with default values or raising an error for further investigation.
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