Which of the following best describes the primary purpose of database normalization?
- Increasing data integrity
- Maximizing redundancy and dependency
- Minimizing redundancy and dependency
- Simplifying data retrieval
Database normalization primarily aims to minimize redundancy and dependency in a database schema, leading to improved data integrity and reducing anomalies such as update, insertion, and deletion anomalies.
Which factor is essential for determining the success of the ETL process?
- Data quality
- Hardware specifications
- Network bandwidth
- Software compatibility
Data quality is an essential factor in determining the success of the ETL (Extract, Transform, Load) process. High-quality data ensures accurate analytics and decision-making, leading to better outcomes.
The use of ________ is essential for tracking lineage and ensuring data quality in Data Lakes.
- Data Catalog
- Data Profiling
- Data Stewardship
- Metadata
Metadata is crucial in Data Lakes for tracking lineage, understanding data origins, and ensuring data quality by providing information about the structure, meaning, and context of the stored data, facilitating its discovery, understanding, and usability.
The process of ensuring data consistency and correctness in real-time data processing systems is known as ________.
- Data integrity
- Data reconciliation
- Data validation
- Data verification
The process of ensuring data consistency and correctness in real-time data processing systems is known as data integrity. Data integrity mechanisms help maintain the accuracy, reliability, and validity of data throughout its lifecycle, from ingestion to analysis and storage. This involves enforcing constraints, validations, and error handling to prevent data corruption or inaccuracies.
________ is a metric commonly monitored to assess the latency of data processing in a pipeline.
- CPU utilization
- Disk space usage
- End-to-end latency
- Throughput
End-to-end latency is a commonly monitored metric in data pipeline monitoring to assess the time it takes for data to traverse the pipeline from its source to its destination. It measures the overall delay or latency experienced by data as it moves through various stages of processing within the pipeline. Monitoring end-to-end latency helps ensure timely data delivery and identifies potential performance bottlenecks or delays in the pipeline.
Which feature is commonly found in data modeling tools like ERWin or Visio to ensure consistency and enforce rules in the design process?
- Data dictionaries
- Data validation
- Reverse engineering
- Version control
Data modeling tools often incorporate data validation features to ensure consistency and enforce rules during the design process. This helps maintain the integrity and quality of the database schema.
How does Apache Airflow handle retries and error handling in workflows?
- Automatic retries with customizable settings, configurable error handling policies, task-level retries
- External retry management through third-party tools, basic error logging functionality
- Manual retries with fixed settings, limited error handling options, workflow-level retries
- No retry mechanism, error-prone execution, lack of error handling capabilities
Apache Airflow provides robust mechanisms for handling retries and errors in workflows. It offers automatic retries for failed tasks with customizable settings such as retry delay and maximum retry attempts. Error handling policies are configurable at both the task and workflow levels, allowing users to define actions to take on different types of errors, such as retrying, skipping, or failing tasks. Task-level retries enable granular control over retry behavior, enhancing workflow resilience and reliability.
What is the difference between data profiling and data monitoring in the context of data quality assessment?
- Data profiling analyzes the structure and content of data at a static point in time, while data monitoring continuously observes data quality over time.
- Data profiling assesses data accuracy, while data monitoring assesses data completeness.
- Data profiling focuses on identifying outliers, while data monitoring identifies data trends.
- Data profiling involves data cleansing, while data monitoring involves data validation.
Data profiling involves analyzing the structure, content, and quality of data to understand its characteristics at a specific point in time. It helps identify data anomalies, patterns, and inconsistencies, which are essential for understanding data quality issues. On the other hand, data monitoring involves continuously observing data quality over time to detect deviations from expected patterns or thresholds. It ensures that data remains accurate, consistent, and reliable over time, allowing organizations to proactively address data quality issues as they arise.
Which metric evaluates the accuracy of data against a trusted reference source?
- Accuracy
- Consistency
- Timeliness
- Validity
Accuracy is a data quality metric that assesses the correctness and precision of data against a trusted reference source. It involves comparing the data values in a dataset with known or authoritative sources to determine their level of agreement. Accurate data ensures that information is reliable and dependable for decision-making and analysis purposes.
In normalization, the process of breaking down a large table into smaller tables to reduce data redundancy and improve data integrity is called ________.
- Aggregation
- Compaction
- Decomposition
- Integration
Normalization involves decomposing a large table into smaller, related tables to eliminate redundancy and improve data integrity by reducing the chances of anomalies.