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.
To handle complex logic, ETL processes may use a ________ transformation, which allows custom code or scripts.
- Advanced
- Complex
- Custom
- Script
In ETL processes, a "Script" transformation is employed to handle complex logic. This transformation allows the use of custom code or scripts to manipulate and transform data according to specific requirements.
Real-time data integration testing often requires ________ to handle high-volume data scenarios.
- Load Balancing
- Parallel Processing
- Replication
- Scalability
Real-time data integration testing often requires Scalability to handle high-volume data scenarios. Scalability ensures that the system can handle increased data loads efficiently, maintaining performance and responsiveness.
What is the significance of Star Schema in Data Warehousing?
- It emphasizes denormalized data for simplicity.
- It focuses on normalizing data to minimize redundancy.
- It is designed for complex relationships between multiple fact and dimension tables.
- It provides a simple structure with a central fact table connected to dimension tables.
The Star Schema in Data Warehousing is significant as it provides a simple structure with a central fact table connected to dimension tables. This design simplifies queries and enhances performance.
During peak data loads, the ETL process slows down significantly. What metrics should be analyzed to identify bottlenecks?
- CPU Utilization, Disk I/O, Memory Usage, Network Bandwidth
- Data Quality Score, Data Latency, Data Duplication Rate, Data Partitioning
- Source Data Volume, Target Data Volume, ETL Tool License Usage, Data Compression Ratio
- Source-to-Target Mapping, Data Encryption Overhead, Data Archiving Efficiency, Data Masking Performance
To identify bottlenecks during peak data loads, metrics such as CPU utilization, disk I/O, memory usage, and network bandwidth should be analyzed. These metrics help pinpoint resource constraints affecting ETL performance.
How does the concept of data variety affect Big Data testing strategies?
- Dealing with large volumes of data
- Ensuring data security
- Handling diverse data types and structures
- Managing data velocity
Data variety in Big Data refers to the diverse types and structures of data, such as structured, unstructured, and semi-structured data. Testing strategies must accommodate this variety to ensure comprehensive validation of all data types.
A retail company is extracting data from various sources for market analysis. What should be the key focus in their extraction technique for accurate trend prediction?
- Data Consistency
- Data Quality
- Data Variety
- Data Volume
In the context of market analysis, the key focus in data extraction should be on ensuring Data Quality. High-quality data ensures accuracy in trend prediction and decision-making. It involves verifying data integrity, removing duplicates, and standardizing formats across different sources.
In data governance, ________ ensures that data usage complies with internal and external regulations.
- Data cataloging
- Data compliance
- Data masking
- Data stewardship
Data stewardship in data governance ensures that data usage complies with internal and external regulations. It involves defining and enforcing data policies to maintain data quality and compliance.