Freshers / Beginner level questions & answers
Ques 1. What is ETL testing?
ETL (Extract, Transform, Load) testing is a process of verifying the correctness of data transformation and loading from source to target databases.
Example:
An example of ETL testing is validating that data from a CSV file is accurately transformed and loaded into a data warehouse.
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Ques 2. What is a staging area in ETL?
A staging area in ETL is an intermediate storage area where data is temporarily held during the extraction and transformation processes before being loaded into the target data warehouse.
Example:
Data extracted from source systems is first loaded into the staging area, where it undergoes transformations before being moved to the final data warehouse.
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Intermediate / 1 to 5 years experienced level questions & answers
Ques 3. Explain the difference between ETL testing and database testing.
ETL testing focuses on the data transformation and loading processes, ensuring data integrity during these processes. Database testing, on the other hand, verifies the correctness of data storage, retrieval, and manipulation within a database.
Example:
In ETL testing, you would check if data is transformed and loaded accurately, while in database testing, you might validate SQL queries and stored procedures.
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Ques 4. What is data profiling in ETL testing?
Data profiling is the process of analyzing and examining source data to understand its structure, quality, and relationships, helping in designing effective ETL processes.
Example:
Data profiling can involve checking for missing values, identifying data patterns, and assessing data distribution in the source system.
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Ques 5. Explain the importance of data cleansing in ETL.
Data cleansing involves identifying and correcting errors or inconsistencies in source data, ensuring the accuracy and reliability of data in the target data warehouse.
Example:
Removing duplicate records and correcting misspelled names in source data are examples of data cleansing activities in ETL.
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Ques 6. What is incremental loading in ETL?
Incremental loading is a technique where only the new or changed data since the last ETL run is extracted, transformed, and loaded into the target system, reducing processing time and resource usage.
Example:
Instead of reloading the entire dataset, only records that have been added or modified since the last load are processed in incremental loading.
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Ques 7. What are the common challenges in ETL testing?
Common challenges in ETL testing include data completeness, data accuracy, performance testing, handling large volumes of data, and managing metadata.
Example:
Ensuring that all records are loaded, validating transformation rules, and optimizing performance for large datasets are challenges in ETL testing.
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Ques 8. What is data reconciliation in ETL testing?
Data reconciliation involves comparing the data in the source system with the data in the target system to ensure that they match, identifying and resolving any discrepancies.
Example:
Comparing the total number of records in the source and target systems is a simple form of data reconciliation in ETL testing.
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Ques 9. What is surrogate key in ETL?
A surrogate key is a unique identifier assigned to a record in the target system to maintain consistency and integrity during the ETL process.
Example:
In a data warehouse, a surrogate key may be assigned to each record to simplify data management and improve performance during ETL operations.
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Ques 10. Explain the concept of data lineage in ETL.
Data lineage is the tracking and visualization of the flow of data from its source through various transformations to its final destination in the target system.
Example:
Creating a data lineage diagram helps understand the path and transformations applied to data from its origin to its use in the data warehouse.
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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.
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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.
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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.
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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.
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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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Ques 16. What is the purpose of a data dictionary in ETL testing?
A data dictionary in ETL testing is a repository that contains metadata information about the data sources, transformations, and target data structures. It provides a centralized reference for understanding the data used in ETL processes.
Example:
A data dictionary may include details such as column names, data types, and transformation rules, aiding in the documentation and understanding of ETL workflows.
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Ques 17. What is CDC (Change Data Capture) in the context of ETL?
Change Data Capture is a technique used in ETL to identify and capture changes made to source data since the last ETL run. It enables the extraction and processing of only the changed data, reducing processing time.
Example:
Using CDC, you can identify new, updated, or deleted records in the source system and apply corresponding changes to the target data warehouse.
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Ques 18. How do you perform performance testing in ETL processes?
Performance testing in ETL involves assessing the efficiency and speed of data extraction, transformation, and loading processes. It may include measuring data load times, resource utilization, and scalability.
Example:
Performance testing may reveal bottlenecks in ETL processes, allowing optimization of SQL queries, parallel processing, or hardware resources for improved performance.
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Ques 19. What is the significance of data encryption in ETL processes?
Data encryption in ETL processes is crucial for securing sensitive information during data transmission and storage. It protects data from unauthorized access and ensures compliance with data security regulations.
Example:
Encrypting Personally Identifiable Information (PII) during data transfer between systems ensures that the data remains confidential and secure.
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Ques 20. Explain the term 'Data Mart' in the context of ETL and data warehousing.
A Data Mart is a subset of a data warehouse that is focused on specific business functions or user groups. It contains a subset of the data warehouse's data, tailored to the needs of a particular department or business unit.
Example:
Creating a sales data mart within a larger data warehouse allows the sales team to access and analyze data relevant to their specific operations.
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