What type of ETL testing is conducted to validate the data aggregation and summarization processes?
- Data Aggregation Testing
- Data Consolidation Testing
- Data Summarization Testing
- Data Transformation Testing
Data Aggregation Testing focuses on validating the accuracy and correctness of the processes involved in aggregating and summarizing data during the ETL transformation phase. It ensures that the summarized data is accurate and meets the intended requirements.
In ETL testing, ________ helps in identifying potential data quality issues early in the process.
- Data extraction
- Data loading
- Data masking
- Data profiling
In ETL testing, data profiling helps in identifying potential data quality issues early in the process. Data profiling involves analyzing source data to understand its structure, quality, and relationships, aiding in the identification of anomalies or inconsistencies.
In ETL, ________ testing is crucial for verifying the transformation rules.
- Integration
- Regression
- Transformation
- Validation
In ETL, Transformation testing is crucial for verifying the accuracy and effectiveness of the transformation rules applied to the data. It ensures that the data is correctly transformed according to the defined business rules.
What are the implications of using real-time data warehousing?
- Improved decision-making with up-to-the-minute data
- Increased data latency and delayed insights
- Limited support for dynamic data sources
- Reduced storage requirements
Real-time data warehousing has implications such as improved decision-making with up-to-the-minute data. However, it may require more resources and careful management due to increased data velocity.
How do data lineage and metadata management contribute to data governance compliance?
- They automate data governance policies
- They improve data storage efficiency
- They provide transparency into data movement and changes
- They secure data from unauthorized access
Data lineage and metadata management contribute to data governance compliance by providing transparency into data movement and changes. This visibility helps ensure that data is handled in accordance with governance policies and regulations.
Future ETL testing must evolve to handle the complexities introduced by ________.
- Big Data
- Legacy Systems
- Microservices
- Real-time Data
Future ETL testing must evolve to handle the complexities introduced by Real-time Data. As organizations increasingly deal with data in real-time, ETL processes need to adapt to ensure timely and accurate data processing.
In a scenario where test cases are frequently missing defects, which part of the Test Execution Lifecycle needs enhancement?
- Test Closure
- Test Design
- Test Execution
- Test Planning
If test cases frequently miss defects, the Test Design phase needs enhancement. It involves creating test cases, and improvements in this phase can lead to better coverage and detection of defects.
After a major ETL update, several unexpected defects arise. What steps should be taken to ensure these are reported and addressed effectively?
- Conduct a thorough impact analysis, document defects with detailed information, and communicate with the development team for resolution
- Ignore the defects unless they cause system crashes
- Report only the defects that are easy to fix
- Wait for user feedback to identify defects
After a major update, conducting a thorough impact analysis is crucial. The testing team should document unexpected defects with detailed information and communicate effectively with the development team for resolution. This proactive approach aids in addressing issues promptly.
________ is crucial for verifying the performance and scalability of a data lake.
- Compatibility testing
- Load testing
- Security testing
- Usability testing
Load testing is crucial for verifying the performance and scalability of a data lake. It involves subjecting the system to simulated usage conditions to assess its ability to handle various loads.
Optimizing Query Performance is essential in ETL to reduce the time taken for data extraction and loading.
- Data Encryption
- Data Modeling
- Indexing
- Network Latency
Query Performance optimization in ETL involves techniques like indexing to speed up data extraction and loading processes. Efficient queries enhance overall ETL performance.