What is the role of a leader election algorithm in distributed systems?
- Ensuring data consistency across nodes
- Load balancing network traffic
- Managing access control permissions
- Selecting a process to coordinate activities
A leader election algorithm in distributed systems is responsible for selecting a process or node to act as the leader, which assumes the responsibility of coordinating activities and making decisions on behalf of the distributed system. The leader plays a crucial role in ensuring orderly execution of distributed algorithms, managing resource allocation, and maintaining system stability. Leader election algorithms help prevent conflicts, establish a single point of authority, and enable efficient coordination among distributed nodes.
In Kafka, the ________ is responsible for storing the committed offsets of the consumers.
- Broker
- Consumer
- Producer
- Zookeeper
In Kafka, Zookeeper is responsible for storing the committed offsets of the consumers. Zookeeper manages various aspects of Kafka's distributed system, including coordination and metadata management.
________ analysis assesses the consistency and correctness of data values within a dataset.
- Data cleansing
- Data integration
- Data profiling
- Data validation
Data profiling analysis involves examining the quality and characteristics of data within a dataset. It assesses various aspects such as consistency, correctness, completeness, and uniqueness of data values. Through data profiling, data engineers can identify anomalies, errors, or inconsistencies in the dataset, which is crucial for ensuring data quality and reliability in subsequent processes like cleansing and integration.
Scenario: Your team is designing a complex data pipeline that involves multiple tasks with dependencies. Which workflow orchestration tool would you recommend, and why?
- AWS Glue - for its serverless ETL capabilities
- Apache Airflow - for its DAG (Directed Acyclic Graph) based architecture allowing complex task dependencies and scheduling
- Apache Spark - for its powerful in-memory processing capabilities
- Microsoft Azure Data Factory - for its integration with other Azure services
Apache Airflow would be recommended due to its DAG-based architecture, which enables the definition of complex workflows with dependencies between tasks. It provides a flexible and scalable solution for orchestrating data pipelines, allowing for easy scheduling, monitoring, and management of workflows. Additionally, Airflow offers a rich set of features such as task retries, logging, and extensibility through custom operators and hooks.
Metadata management tools often use ________ to track changes in data lineage over time.
- Auditing
- Compression
- Encryption
- Versioning
Metadata management tools often use auditing mechanisms to track changes in data lineage over time. Auditing helps monitor modifications, access, and usage of metadata, ensuring accountability, compliance, and data governance. It provides a historical record of metadata changes, facilitating troubleshooting and maintaining data lineage accuracy.
Which consistency model is typically associated with NoSQL databases?
- Causal consistency
- Eventual consistency
- Linearizability
- Strong consistency
NoSQL databases typically adopt the eventual consistency model, where updates to the data propagate asynchronously, providing a higher level of availability and partition tolerance at the expense of consistency.
________ is a data extraction technique that involves extracting data from a source system's log files, typically in real-time.
- API Integration
- Change Data Capture (CDC)
- ELT (Extract, Load, Transform)
- ETL (Extract, Transform, Load)
Change Data Capture (CDC) is a data extraction technique that involves extracting data from a source system's log files in real-time, enabling near real-time analysis and processing of the captured data.
Scenario: Your team is tasked with integrating data from multiple sources into a centralized database. What steps would you take to ensure data consistency and accuracy in the modeling phase?
- Design a robust data integration architecture to handle diverse data sources
- Establish data lineage and documentation processes
- Implement data validation rules and checks to ensure accuracy
- Perform data profiling and cleansing to identify inconsistencies and errors
Ensuring data consistency and accuracy during data integration involves steps such as data profiling and cleansing to identify and rectify inconsistencies, implementing validation rules, and establishing documentation processes to maintain data lineage and traceability.
Scenario: A project requires handling complex and frequently changing business requirements. How would you approach the design decisions regarding normalization and denormalization in this scenario?
- Apply strict normalization to ensure data consistency and avoid redundancy
- Employ a hybrid approach, combining aspects of normalization and denormalization as needed
- Focus on denormalization to optimize query performance and adapt quickly to changing requirements
- Prioritize normalization to maintain data integrity and flexibility, adjusting as business requirements evolve
In a project with complex and frequently changing business requirements, a hybrid approach combining elements of both normalization and denormalization is often the most effective. This allows for maintaining data integrity and flexibility while also optimizing query performance and adapting to evolving business needs.
What is the primary purpose of using data modeling tools like ERWin or Visio?
- To design database schemas and visualize data structures
- To execute SQL queries
- To optimize database performance
- To perform data analysis and generate reports
The primary purpose of using data modeling tools like ERWin or Visio is to design database schemas and visualize data structures. These tools provide a graphical interface for creating and modifying database designs, enabling data engineers to efficiently plan and organize their database systems.