In ETL testing, what is the purpose of comparing source and target system data?
- To assess data quality
- To ensure data consistency
- To test data integrity
- To verify data completeness
Comparing source and target system data in ETL testing helps ensure data consistency between the original source and the destination after the ETL process. It validates that data has been accurately extracted, transformed, and loaded without loss or corruption, thereby maintaining data integrity and quality throughout the process.
What challenges arise in Test Data Management when testing ETL processes for big data environments?
- Data Volume Complexity
- Fast Processing
- Limited Data Sources
- Scalability Issues
Testing ETL processes in big data environments introduces challenges related to the complexity of handling large volumes of data. Managing diverse data sources and ensuring accurate transformations become more intricate with the scale of big data.
Advanced data lake testing includes ________, which ensures the correct processing of complex data workflows.
- Data lineage validation
- Metadata validation
- Schema validation
- Workflow validation
Advanced data lake testing involves "Workflow validation," which ensures the correct processing of complex data workflows. This ensures that the data moves through the defined workflow as expected, maintaining accuracy and integrity.
In Big Data testing, what is commonly tested to ensure the system can handle large volumes of data?
- Data Quality
- Functionality
- Scalability
- Security
Scalability is commonly tested in Big Data testing to ensure the system can handle large volumes of data. This involves assessing the system's ability to scale and perform well as the volume of data increases.
________ is a methodology used in Data Warehousing to update data in a database incrementally.
- CDC (Change Data Capture)
- ETL (Extract, Transform, Load)
- OLAP (Online Analytical Processing)
- SQL (Structured Query Language)
Change Data Capture (CDC) is a methodology used in Data Warehousing to update data incrementally. It identifies and captures changes made to source data since the last update, reducing the processing load.
During ETL testing, ________ testing is performed to validate the metadata.
- Integration
- Metadata
- Regression
- Unit
During ETL testing, Metadata testing is performed to validate the metadata, ensuring that the information about data structure, relationships, and formatting is accurate and consistent.
In advanced BI integrations, what role does ETL play in ensuring data governance?
- Data Integration
- Data Migration
- Data Profiling
- Data Security
In advanced BI integrations, ETL plays a crucial role in ensuring data governance through data integration. ETL processes enforce data quality, consistency, and security, promoting governance by adhering to predefined rules and standards.
In ETL testing, comparing record counts and key aggregates between source and target is known as ________.
- Data Matching
- Data Profiling
- Data Reconciliation
- Data Validation
Data reconciliation involves comparing record counts and key aggregates between the source and target systems to ensure that the data is accurately transformed and loaded. It is an essential step in ETL testing.
________ plays a key role in managing and protecting sensitive data within an organization.
- Data cataloging
- Data encryption
- Data masking
- Data profiling
Data encryption plays a key role in managing and protecting sensitive data within an organization. It involves encoding data in a way that only authorized users can access and understand it, enhancing data security.
The use of ________ in data extraction helps in handling semi-structured data.
- Data Lakes
- Data Warehouse
- NoSQL databases
- XML
The use of NoSQL databases in data extraction helps in handling semi-structured data. NoSQL databases, such as MongoDB or Cassandra, are well-suited for accommodating flexible and varying data structures.