When testing a Big Data system for a healthcare application, what factors must be considered to maintain data privacy and accuracy?
- Data Encryption, Role-Based Access Control, and Anonymization
- Functional Testing, Regression Testing, and User Acceptance Testing
- Performance Testing, Load Testing, and Stress Testing
- Scalability Testing, Latency Testing, and Concurrency Testing
Ensuring data privacy in a healthcare Big Data system involves techniques like Data Encryption, Role-Based Access Control, and Anonymization. These measures protect sensitive information and maintain data accuracy.
What is the primary goal of real-time data integration testing?
- Accuracy of Real-time Processing
- Data Completeness
- Data Consistency
- Timeliness of Data
The primary goal of real-time data integration testing is to ensure the accuracy of real-time processing. This involves verifying that the data being integrated in real-time is accurate and reliable, meeting the requirements of the system.
In ETL testing, the use of __________ can significantly reduce the risk of data corruption during transformation.
- Change data capture
- Data checksums
- Data lineage
- Parallel processing
The use of Data Lineage in ETL testing is crucial for understanding the flow of data from source to destination. It helps identify dependencies and ensures data integrity, thereby reducing the risk of data corruption during the transformation process.
During a data governance audit, a company discovers data inconsistencies. What steps should be taken to align with best practices in data governance?
- Hiring additional data analysts
- Ignoring the inconsistencies temporarily
- Implementing data quality controls
- Suspending all data-related activities
Implementing data quality controls is the appropriate step to align with best practices in data governance when data inconsistencies are discovered during an audit. Data quality controls help identify, correct, and prevent data inaccuracies, ensuring the integrity and reliability of the data, which is essential for effective data governance.
How does change data capture (CDC) impact the ETL process?
- Enables real-time data integration
- Improves data extraction
- Reduces the need for data transformation
- Speeds up data processing
CDC is vital for real-time data integration. It identifies and captures changes in source data since the last extraction, allowing for near-real-time updates in the destination. This impacts the ETL process by enhancing its ability to reflect changes quickly and efficiently.
What role does data masking play in Test Data Management?
- Data compression
- Data encryption
- Data replication
- Hiding sensitive information
Data masking in Test Data Management involves hiding sensitive information within the test environment. It ensures that confidential data is protected during testing while still allowing realistic scenarios to be simulated.
What is the potential impact of the Internet of Things (IoT) on ETL testing practices?
- Decreased need for data validation
- Exclusively structured data for ETL
- Increased volume and variety of data
- Simplification of ETL processes
The potential impact of IoT on ETL testing practices involves dealing with an increased volume and variety of data. IoT devices generate massive amounts of data, challenging ETL processes to handle diverse data formats and structures efficiently.
How does data profiling in ETL testing help in risk management?
- Creating test plans
- Executing test cases
- Identifying anomalies and patterns in data
- Monitoring system performance
Data profiling in ETL testing involves analyzing and understanding the characteristics of data. This helps in identifying anomalies and patterns, allowing for a better understanding of the data and potential risks in the ETL process.
________ supports extensive data connectivity, including traditional databases, cloud services, and big data platforms.
- Apache Nifi
- Informatica PowerCenter
- Microsoft SSIS
- Oracle Data Integrator
Informatica PowerCenter supports extensive data connectivity, providing compatibility with traditional databases, cloud services, and big data platforms. This versatility enables organizations to integrate data from diverse sources.
How do ETL processes and BI tools work together to support decision-making?
- BI tools transform data for ETL processes to load
- ETL processes and BI tools are unrelated
- ETL processes and BI tools perform the same function
- ETL processes extract data for BI tools to analyze
ETL processes extract, transform, and load data from various sources into a data warehouse, while BI tools analyze and visualize this data to support decision-making. They work together by providing clean, transformed data for analysis, enabling informed decision-making.
How does compliance with standards like GDPR affect ETL Security Testing?
- It has no impact on ETL Security Testing
- It introduces additional complexity
- It only affects the extraction phase
- It simplifies the testing process
Compliance with standards like GDPR introduces additional complexity to ETL Security Testing. Ensuring data protection and privacy requires thorough testing to meet regulatory requirements.
How does Informatica's dynamic partitioning feature affect ETL performance?
- Enhances data extraction
- Improves parallel processing
- Minimizes data transformations
- Slows down data movement
Informatica's dynamic partitioning feature enhances ETL performance by improving parallel processing. It allows for efficient distribution of data across nodes, optimizing data movement and processing speed.