What is the role of ZooKeeper in the Hadoop ecosystem?
- Coordination, synchronization, and configuration management
- Data processing and analysis
- Data storage and retrieval
- Resource management and scheduling
ZooKeeper in the Hadoop ecosystem serves as a centralized coordination service, providing functionalities such as distributed synchronization, configuration management, and distributed naming.
In an RDBMS, a ________ is a virtual table that represents the result of a database query.
- Cursor
- Index
- Trigger
- View
A View in an RDBMS is a virtual table that represents the result of a database query. It does not store data itself but displays data from one or more tables based on specified criteria.
The process of persisting intermediate data in memory to avoid recomputation in Apache Spark is called ________.
- Caching
- Checkpointing
- Repartitioning
- Serialization
In Apache Spark, the process of persisting intermediate data in memory to avoid recomputation is known as caching. This technique enhances performance by storing RDDs or DataFrames in memory for reuse in subsequent operations, reducing the need for recomputation.
________ evaluates the reliability of data in terms of its trustworthiness.
- Data Authenticity
- Data Integrity
- Data Provenance
- Data Security
Data Authenticity evaluates the reliability of data in terms of its trustworthiness. It ensures that data originates from a credible source and has not been tampered with or altered during transmission or storage. Maintaining data authenticity is essential for ensuring the integrity and reliability of data-driven processes and decisions.
In ETL optimization, ________ techniques are used to identify and eliminate redundant or unnecessary data transformations.
- Indexing
- Normalization
- Partitioning
- Profiling
In ETL optimization, profiling techniques are used to analyze data sources and identify patterns, redundancies, and anomalies, enabling the elimination of unnecessary transformations.
How does circuit breaking enhance the reliability of error handling in distributed systems?
- By eliminating the need for error retries
- By increasing the complexity of error handling
- By preventing cascading failures
- By reducing network latency
Circuit breaking enhances the reliability of error handling in distributed systems by preventing cascading failures. It works by monitoring the health of downstream services and temporarily halting requests if a certain threshold of failures is exceeded. This prevents overloading and potentially crashing downstream services, allowing them time to recover and reducing the impact on the entire system. By isolating failing components, circuit breaking helps maintain system stability and resilience in the face of failures.
Database administrators often use ________ to identify unused or redundant indexes and optimize database performance.
- Database normalization
- Index fragmentation
- Index tuning
- Query optimization
Database administrators often use index tuning to identify unused or redundant indexes and optimize database performance. This involves analyzing query patterns and the effectiveness of existing indexes to make informed decisions.
Which mechanism ensures that failed tasks are retried automatically in case of errors?
- Backpressure
- Checkpointing
- Resilience
- Retry Policies
Retry mechanisms ensure that failed tasks are retried automatically in case of errors. By implementing retry policies, data processing systems can recover from transient failures, such as network issues or temporary resource constraints, without manual intervention. This ensures fault tolerance and improves the overall robustness and reliability of data pipelines and processing workflows.
Scenario: A company is planning to implement a data governance framework to address data privacy concerns. Which regulatory compliance should they focus on, and how can the framework help in achieving compliance?
- CCPA (California Consumer Privacy Act); By enabling transparency in data collection practices, providing opt-out options for consumers, and ensuring data security and integrity.
- GDPR (General Data Protection Regulation); By establishing policies for data handling, ensuring consent management, and implementing mechanisms for data subject rights.
- HIPAA (Health Insurance Portability and Accountability Act); By implementing measures for securing Protected Health Information (PHI) and ensuring privacy in healthcare data handling.
- PCI DSS (Payment Card Industry Data Security Standard); By implementing controls to protect payment card data, ensuring secure transmission and storage of cardholder information.
GDPR (General Data Protection Regulation) is a crucial regulatory compliance framework that organizations should focus on to address data privacy concerns. It requires organizations to implement measures for lawful, fair, and transparent processing of personal data, as well as ensuring data subjects' rights and freedoms. A data governance framework can help achieve GDPR compliance by establishing clear policies and procedures for data handling, ensuring consent management processes, and implementing mechanisms to uphold data subjects' rights, such as the right to access and erasure of personal data.
In data modeling, what does the term "Normalization" refer to?
- Adding redundancy to data
- Denormalizing data
- Organizing data in a structured manner
- Storing data without any structure
In data modeling, "Normalization" refers to organizing data in a structured manner by reducing redundancy and dependency, leading to an efficient database design that minimizes data anomalies.