________ in ETL involves ensuring that data is consistent and correct across the system.
- Cleansing
- Standardization
- Transformation
- Validation
The Validation step in ETL is responsible for ensuring that data is consistent and correct throughout the system. It involves checks and validation rules to maintain data quality.
Manual testing is often preferred when ________ is a key requirement.
- Complexity
- Precision
- Repeatability
- Speed
Manual testing is often preferred when Repeatability is a key requirement. In certain situations where test cases need to be executed with various data sets or under changing conditions, manual testing provides flexibility and adaptability.
In ETL, ________ is used to extract data from different sources.
- Extraction
- Integration
- Loading
- Transform
In ETL, the process of Extraction is used to gather data from various sources. It is the initial step where data is collected for further processing in the ETL pipeline.
In a scenario where an organization must comply with GDPR, how do data quality tools assist in maintaining compliance?
- Apply data profiling techniques
- Enforce data masking policies
- Ensure data anonymity
- Implement encryption algorithms
Data quality tools assist in GDPR compliance by ensuring data anonymity. They can anonymize sensitive information, protecting privacy and meeting regulatory requirements without compromising data quality.
When an ETL process experiences latency issues during peak loads, what should be analyzed using performance testing tools?
- ETL Server Performance
- Network Latency
- Source System Performance
- Target System Performance
During peak loads, analyzing the performance of the ETL server is crucial. This involves assessing the server's capacity, resource utilization, and response times to identify bottlenecks and optimize performance.
What is the role of version control systems in ETL testing?
- Controlling data versions
- Managing ETL server versions
- Tracking changes to ETL code and configurations
- Version control systems are not relevant to ETL testing
Version control systems play a crucial role in ETL testing by tracking changes to ETL code and configurations. This ensures traceability, collaboration, and the ability to revert to previous versions if needed.
In the context of data verification, what is the importance of referential integrity?
- It ensures data is within acceptable ranges
- It maintains consistency in relationships between tables
- It validates data types and formats
- It verifies the accuracy of individual records
Referential integrity is crucial for maintaining consistency in relationships between tables. It ensures that relationships between foreign keys and primary keys are valid, preventing orphaned or inconsistent data.
How does regression testing differ in Agile methodology compared to traditional ETL processes?
- Agile focuses more on regression testing
- Agile has no regression testing
- No difference, it remains the same
- Traditional ETL processes emphasize more on regression testing
In Agile, regression testing is integrated throughout the development process, ensuring continuous testing with every iteration. Traditional ETL processes may have a separate regression testing phase. The difference lies in the integration and frequency of testing in Agile.
A company is implementing AI/ML in its ETL testing process to handle large data volumes. What are the expected benefits and challenges?
- Efficient Resource Utilization, Reduced Processing Time, Enhanced Data Privacy, Dependency on Legacy Systems
- Faster Processing, Reduced Cost, Data Security, Increased Manual Efforts
- Improved Scalability, Enhanced Data Accuracy, Increased Automation, Potential for Bias
- Real-time Monitoring, Simplified Maintenance, Decreased Complexity, Limited Data Exploration
Implementing AI/ML in ETL testing for large data volumes can bring benefits like improved scalability, enhanced data accuracy, increased automation, but it also poses challenges such as potential bias in algorithms and the need for thorough validation.
In the context of big data, what are the challenges associated with data extraction?
- All of the above
- Variety of data sources
- Velocity of data
- Volume of data
The challenges associated with data extraction in big data include handling large volumes of data, dealing with a variety of data sources, and managing the high velocity at which data is generated. Addressing these challenges is crucial for successful big data extraction.