For real-time data, ________ testing is critical to ensure timely data availability and accuracy in a data lake.
- Aggregation
- Conformance
- Ingestion
- Transformation
For real-time data in a data lake, "Ingestion" testing is critical. It focuses on validating the process of ingesting data in real-time, ensuring timely availability and accuracy for downstream processing.
An Agile team is facing challenges with non-reproducible ETL test failures. What strategy should be implemented for effective troubleshooting?
- Collaborate with developers to understand the system changes and enhance test data generation for better reproducibility
- Document the non-reproducible failures and ignore them as outliers
- Rerun the failed tests with the same test data to reproduce the issue
- Skip troubleshooting and proceed with other testing activities
When facing non-reproducible ETL test failures in Agile, it's crucial to collaborate with developers to understand system changes and enhance test data generation. This ensures effective troubleshooting and identifies the root cause for resolution.
In terms of ETL, how do advanced data quality tools handle complex data transformations?
- Ignore complex transformations for simplicity
- Leverage pre-built functions and algorithms for common transformations
- Rely solely on manual intervention for complex transformations
- Utilize custom scripts and code for specific transformations
Advanced data quality tools in ETL often employ custom scripts and code to handle complex data transformations, ensuring flexibility and precision in processing diverse data structures and formats.
The trend towards ________ in ETL signifies the shift to more agile and scalable data integration methods.
- Cloud Integration
- DevOps
- Edge Computing
- Microservices Architecture
The trend towards Microservices Architecture in ETL signifies the shift to more agile and scalable data integration methods, allowing for modular and independent components that enhance flexibility and efficiency.
A ________ is a subset of a Data Warehouse that is focused on a specific business line or team.
- Data Cube
- Data Mart
- Data Repository
- Data Silo
A Data Mart is a subset of a Data Warehouse that is focused on a specific business line or team. It contains data relevant to a particular business area, making it easier to analyze and extract insights.
What should be considered when replicating production data in a test environment for ETL?
- All of the above
- Data volume differences
- Security concerns
- Use of synthetic data
When replicating production data in a test environment for ETL, considerations should include data volume differences. It's crucial to account for variations in data volume to ensure the effectiveness of the testing process.
What role does data masking play in ETL Security Testing?
- Data compression for storage
- Data encryption during transmission
- Data profiling
- Hiding sensitive information
Data masking in ETL Security Testing involves hiding sensitive information, ensuring that only authorized users can access and view confidential data. It's a crucial aspect for compliance with privacy regulations.
Which transformation step is essential for normalizing data from various sources into a standard format?
- Aggregation
- Joining
- Normalization
- Sorting
Normalization is the transformation step essential for standardizing data from various sources into a common format. It eliminates redundancy and organizes data to avoid anomalies.
In a Data Warehouse, what is the role of an OLAP (Online Analytical Processing) server?
- Data Analysis
- Data Extraction
- Data Loading
- Data Transformation
In a Data Warehouse environment, the OLAP server is responsible for performing complex analytical and ad-hoc queries on the data. It facilitates multidimensional analysis, enabling users to explore data from different perspectives and gain insights through interactive reporting and visualization.
Which type of testing is essential for validating the processing speed and efficiency of a Big Data application?
- Functional Testing
- Performance Testing
- Regression Testing
- Security Testing
Performance Testing is essential for validating the processing speed and efficiency of a Big Data application. It assesses how well the system performs under various conditions, especially when dealing with massive amounts of data.