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.
________ tools are often used in ETL for automated data validation and error detection.
- Data Integration
- Data Migration
- Data Profiling
- Data Quality
Data Quality tools are commonly utilized in ETL processes for automated data validation and error detection. These tools ensure that the data meets predefined quality standards and help identify and rectify any anomalies.
A company is integrating its ETL process with a new BI tool. What factors should be considered for seamless integration?
- Data Compatibility, Performance, Metadata Management, Scalability
- Data Governance, Data Backup, Cloud Integration, ETL Logging
- Data Quality, Network Latency, Data Encryption, User Interface
- Data Security, Real-time Processing, Hardware Compatibility, Data Compression
Factors such as Data Compatibility, Performance, Metadata Management, and Scalability are crucial for seamless integration between ETL processes and BI tools. Ensuring these elements align will contribute to efficient data flow and analysis.
In the context of ETL testing, what does regression testing primarily focus on?
- Checking for performance bottlenecks
- Ensuring backward compatibility
- Validating data integrity
- Verifying the new features
Regression testing in ETL focuses on ensuring backward compatibility by checking that changes or additions to the ETL system do not negatively impact existing functionalities. It helps maintain the overall integrity of the ETL process.
After test execution, what is the next step in the Test Execution Lifecycle?
- Test Closure
- Test Design
- Test Execution
- Test Planning
The next step after test execution is Test Closure. In this phase, the testing activities are formally concluded. It involves preparing test summary reports, documenting lessons learned, and obtaining approval to close the testing phase.
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.
In complex ETL processes, ________ testing ensures the consistency of data between source and target systems.
- Data Consistency
- Integration
- Performance
- Regression
In complex ETL processes, Data Consistency testing ensures the consistency of data between source and target systems, especially when multiple transformations and manipulations are involved. It helps identify any discrepancies or data integrity issues.
In complex Data Warehousing, ________ is used to manage and store high volumes of diverse data.
- Data Integration
- Data Mining
- Data Profiling
- Hadoop
In complex Data Warehousing, Hadoop is used to manage and store high volumes of diverse data. Hadoop's distributed storage and processing capabilities make it suitable for handling large-scale and varied datasets.