In ETL, the process of splitting a single data column into multiple columns is referred to as ________ transformation.
- Decompose
- Split
- Unpack
- Unpivot
The correct term is "Unpivot" transformation. This involves breaking down a single column with multiple values into separate columns, each representing a specific attribute. It is useful for organizing and structuring data for analysis.
A company notices that its nightly ETL jobs are taking longer to complete than expected. What should be the first step in diagnosing and optimizing the performance?
- Adding more source systems
- Increasing server capacity
- Reviewing ETL job logs and performance metrics
- Rewriting ETL code from scratch
The first step in diagnosing and optimizing ETL performance is to review ETL job logs and performance metrics. This helps identify bottlenecks, such as slow-running queries or resource constraints, that may be causing delays.
________ in SQL are used to ensure the database performs actions in a logical order.
- Constraints
- Indexes
- Transactions
- Views
In SQL, Transactions are used to ensure that a series of database actions are performed in a logical order. Transactions provide consistency and help maintain the integrity of the database.
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.