How does data lineage contribute to regulatory compliance in metadata management?
- By automating data backups
- By encrypting sensitive data
- By optimizing database performance
- By providing a clear audit trail of data transformations and movements
Data lineage traces the flow of data from its source through various transformations to its destination, providing a comprehensive audit trail. This audit trail is crucial for regulatory compliance as it ensures transparency and accountability in data handling processes, facilitating easier validation of data for regulatory purposes.
What does a diamond shape in an ERD signify?
- Attribute
- Entity
- Primary Key
- Relationship
A diamond shape in an Entity-Relationship Diagram (ERD) signifies a relationship between entities. It represents how entities are related to each other in the database model.
What does the acronym ETL stand for in data engineering?
- Extend, Transfer, Load
- Extract, Transfer, Load
- Extract, Transform, List
- Extract, Transform, Load
ETL stands for Extract, Transform, Load. It refers to the process of extracting data from various sources, transforming it into a consistent format, and loading it into a target destination for analysis or storage.
Which of the following is a primary purpose of indexing in a database?
- Enforcing data integrity
- Improving the speed of data retrieval
- Reducing storage space
- Simplifying database administration
Indexing in a database primarily serves to enhance the speed of data retrieval by creating a structured mechanism for locating data, often using B-tree or hash-based data structures.
Which component of Apache Spark is responsible for scheduling tasks across the cluster?
- Spark Driver
- Spark Executor
- Spark Master
- Spark Scheduler
The Spark Scheduler is responsible for scheduling tasks across the cluster. It allocates resources and manages the execution of tasks on worker nodes, ensuring efficient utilization of cluster resources.
Scenario: Your team is tasked with designing a system to handle real-time analytics on social media interactions. Which type of NoSQL database would you recommend, and why?
- Column Store
- Document Store
- Graph Database
- Key-Value Store
For real-time analytics on social media interactions, a Graph Database would be recommended. It's suitable for representing complex relationships between entities like users, posts, and interactions, facilitating efficient query processing.
Scenario: Your team is experiencing slow query performance in a production database. Upon investigation, you find that there are no indexes on the columns frequently used in the WHERE clause of queries. What would be your recommended solution to improve query performance?
- Create Indexes on the frequently used columns
- Increase server memory
- Optimize SQL queries
- Upgrade database hardware
To improve query performance, creating indexes on the columns frequently used in the WHERE clause can significantly reduce the time taken for query execution by allowing the database engine to quickly locate the relevant rows.
In which scenario would you consider using a non-clustered index over a clustered index?
- When you frequently query a large range of values
- When you need to enforce a primary key constraint
- When you need to physically reorder the table data
- When you want to ensure data integrity
A non-clustered index is considered when you frequently query a large range of values or when you want to avoid the overhead of reordering the physical data in the table, which is required by a clustered index.
What are the challenges associated with Data Lake implementation?
- Data integration difficulties
- Ingestion complexities
- Lack of data governance
- Scalability issues
Challenges in Data Lake implementation often include the lack of data governance, which can lead to issues related to data quality, consistency, and compliance. Ensuring proper governance mechanisms is crucial for maintaining the integrity and reliability of data within the Data Lake.
What is the primary purpose of workflow orchestration tools like Apache Airflow and Luigi?
- Creating interactive data visualizations
- Developing machine learning models
- Managing and scheduling complex data workflows
- Storing and querying large datasets
Workflow orchestration tools like Apache Airflow and Luigi are primarily designed to manage and schedule complex data workflows. They allow data engineers to define, schedule, and monitor workflows consisting of multiple tasks or processes, facilitating the automation and orchestration of data pipelines. These tools provide features such as task dependencies, retry mechanisms, and monitoring dashboards, enabling efficient workflow management and execution.