A healthcare organization needs to extract patient data from various legacy systems. What strategy should be employed for effective and secure data extraction?
- Full Extraction
- Incremental Extraction
- Parallel Extraction
- Serial Extraction
For effective and secure extraction of patient data from legacy systems, Incremental Extraction strategy should be employed. This approach extracts only the data that has changed since the last extraction, reducing the load on systems and minimizing the risk of errors or data loss.
How does automated regression testing in ETL differ from manual regression testing?
- Accuracy
- Complexity Handling
- Resource Requirements
- Speed and Efficiency
Automated regression testing in ETL is faster and more efficient compared to manual testing. Automation allows for quicker execution of test cases, enabling comprehensive regression testing in a shorter time frame.
Advanced ETL testing scenarios often require version control features like ________ to manage multiple versions of test scripts effectively.
- Branching
- Committing
- Merging
- Pull requests
Branching in version control systems is essential for managing multiple versions of test scripts effectively. It allows testers to work on separate branches, making changes without affecting the main codebase until ready for integration.
A financial institution is implementing a new data governance framework. What should be the primary focus to ensure compliance with international financial regulations?
- Conducting periodic data backups
- Enhancing data visualization tools
- Establishing data lineage and traceability
- Implementing data encryption techniques
Establishing data lineage and traceability should be the primary focus to ensure compliance with international financial regulations. This involves documenting the origin and movement of data throughout its lifecycle, ensuring transparency and accountability in data handling processes, which is crucial for regulatory compliance.
In the ETL process, which step involves cleaning and transforming the extracted data for loading?
- Cleanse
- Extract
- Load
- Transform
In the ETL process, the "Transform" step involves cleaning and transforming the extracted data to ensure it meets the quality and structure requirements of the target system before loading.
A company is setting up a test environment for its new ETL solution. What factors should they consider to ensure the environment is effective for performance testing?
- Data modeling, ETL tool licensing, Database schema, Data compression
- Database indexes, Data security, Source system uptime, Data redundancy
- Hardware specifications, Data volume, Network latency, Concurrent users
- Software versions, Data profiling, Data encryption, Data governance policies
For effective performance testing, factors like hardware specifications, data volume, and network latency should be considered. These elements impact the efficiency of the ETL solution under various conditions, ensuring it meets performance requirements.
How does the concept of data lake zones affect testing strategies?
- Data lake zones encrypt all stored data
- Data lake zones partition data based on its intended use
- Data lake zones prioritize data based on its age
- Data lake zones segregate data based on its size
Data lake zones partition data based on its intended use, such as raw data, curated data, or processed data. Testing strategies need to consider the specific characteristics and requirements of each zone to ensure comprehensive testing coverage.
Which tool is commonly used for basic performance testing in data processing?
- Apache JMeter
- Git
- JIRA
- Selenium
Apache JMeter is commonly used for basic performance testing in data processing. It allows testers to simulate various scenarios and analyze the performance of the ETL process under different conditions.
In ETL testing, how is a missing value typically categorized?
- Blank Value
- Empty Value
- Null Value
- Void Value
A missing value in ETL testing is typically categorized as a Null Value. Null values represent the absence of data and are important to detect and handle during the testing process for accurate data processing.
What is the role of a split transformation in ETL?
- It combines multiple data streams into a single stream
- It divides a single data stream into multiple streams based on specified criteria
- It performs data cleansing operations
- It validates data integrity
The role of a split transformation in ETL is to divide a single data stream into multiple streams based on specified criteria. This allows for parallel processing or routing of data to different destinations based on conditions such as value ranges, business rules, or destination targets.