Which of the following best describes metadata in the context of data lineage?
- Data validation rules
- Descriptive information about data attributes and properties
- Encrypted data stored in databases
- Historical data snapshots
Metadata, in the context of data lineage, refers to descriptive information about data attributes and properties. It includes details such as data source, format, schema, relationships, and transformations applied to the data. Metadata provides context and meaning to the data lineage, enabling users to understand and interpret the lineage information effectively. It plays a crucial role in data governance, data integration, and data management processes.
Which type of relationship in an ERD indicates that each instance of one entity can be associated with multiple instances of another entity?
- Many-to-Many
- Many-to-One
- One-to-Many
- One-to-One
In an ERD, a Many-to-Many relationship indicates that each instance of one entity can be associated with multiple instances of another entity, and vice versa, allowing for complex associations between entities.
What role does Apache Cassandra play in big data storage solutions?
- Data warehousing solution
- NoSQL distributed database management system
- Search engine platform
- Stream processing framework
Apache Cassandra serves as a NoSQL distributed database management system in big data storage solutions. It is designed for high scalability and fault tolerance, allowing for the storage and retrieval of large volumes of structured and semi-structured data across multiple nodes in a distributed manner. Cassandra's decentralized architecture and support for eventual consistency make it well-suited for use cases requiring high availability, low latency, and linear scalability, such as real-time analytics, IoT data management, and messaging applications.
Scenario: Your organization is experiencing performance issues with its ETL pipeline, resulting in delayed data processing. As an ETL specialist, what steps would you take to diagnose and address these performance issues?
- Analyze and optimize data ingestion and loading processes.
- Implement data partitioning and sharding strategies.
- Increase hardware resources such as CPU and memory.
- Review and optimize data transformation logic and SQL queries.
To address performance issues in an ETL pipeline, reviewing and optimizing data transformation logic and SQL queries is essential. This involves identifying inefficient queries or transformations and optimizing them for better performance.
Apache MapReduce divides tasks into ________ and ________ phases for processing large datasets.
- Input, Output
- Map, Reduce
- Map, Shuffle
- Sort, Combine
Apache MapReduce divides tasks into Map and Reduce phases for processing large datasets. The Map phase handles input data and generates key-value pairs, while the Reduce phase aggregates and processes these pairs.
Scenario: You are working on a project where data integrity is crucial. Your team needs to design a data loading process that ensures data consistency and accuracy. What steps would you take to implement effective data validation in the loading process?
- Data Profiling
- Referential Integrity Checks
- Row Count Validation
- Schema Validation
Referential integrity checks ensure that relationships between data tables are maintained, preventing orphaned records and ensuring data consistency. By verifying the integrity of foreign key relationships, this step enhances data accuracy and reliability during the loading process.
Apache Flink's ________ API enables complex event processing and time-based operations.
- DataSet
- DataStream
- SQL
- Table
Apache Flink's DataStream API is designed for processing unbounded streams of data, enabling complex event processing and time-based operations such as windowing and event-time processing. It provides high-level abstractions for expressing data transformations and computations on continuous data streams, making it suitable for real-time analytics and stream processing applications.
________ involves comparing data across multiple sources or systems to identify discrepancies and inconsistencies.
- Data integration
- Data profiling
- Data reconciliation
- Data validation
Data reconciliation involves comparing data from different sources or systems to ensure consistency and accuracy. It helps identify discrepancies, such as missing or mismatched data, between datasets. This process is crucial in data integration projects to ensure that data from various sources align properly and can be combined effectively.
Scenario: During a routine audit, it is discovered that employees have been accessing sensitive customer data without proper authorization. What measures should be implemented to prevent unauthorized access and ensure compliance with data security policies?
- Deny the audit findings, hide access logs, manipulate data to conceal unauthorized access, and disregard compliance requirements
- Downplay the severity of unauthorized access, overlook policy violations, prioritize business continuity over security, and avoid disciplinary actions
- Ignore the findings, blame individual employees, restrict access to auditors, and continue operations without changes
- Review and update access controls, enforce least privilege principles, implement multi-factor authentication, conduct regular audits and monitoring, and provide ongoing training on data security policies and procedures
To prevent unauthorized access and ensure compliance with data security policies, organizations should review and update access controls to restrict permissions based on job roles and responsibilities, enforce least privilege principles to limit access to only necessary resources, implement multi-factor authentication for additional security layers, conduct regular audits and monitoring to detect and deter unauthorized activities, and provide ongoing training to employees on data security policies and procedures. By implementing these measures, organizations can strengthen their security posture, mitigate risks, and maintain compliance with regulatory requirements.
Scenario: Your team is tasked with optimizing query performance in a reporting database. Discuss whether you would consider denormalization as part of your optimization strategy and justify your answer.
- No, denormalization can compromise data integrity and increase the risk of anomalies
- No, denormalization can lead to data redundancy and inconsistency, making maintenance challenging
- Yes, denormalization can enhance data aggregation capabilities and streamline complex reporting queries
- Yes, denormalization can improve query performance by reducing the number of joins and simplifying data retrieval
In optimizing query performance for a reporting database, denormalization can be considered as it reduces the need for joins, simplifies data retrieval, and enhances data aggregation capabilities. However, it's crucial to weigh the performance benefits against the potential risks to data integrity and consistency.
What is Apache Spark primarily used for?
- Big data processing
- Data visualization
- Mobile application development
- Web development
Apache Spark is primarily used for big data processing, enabling fast and efficient processing of large datasets across distributed computing clusters. It provides various libraries for diverse data processing tasks.
The process of replicating data across multiple brokers in Kafka is called ________.
- Distribution
- Partitioning
- Replication
- Sharding
The process of replicating data across multiple brokers in Kafka is called Replication. Kafka ensures fault tolerance and reliability by replicating data across multiple brokers in a Kafka cluster.