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.
What are some strategies for optimizing data loading in ETL processes?
- Batch loading, serial processing
- Incremental loading, parallel processing
- Random loading, distributed processing
- Sequential loading, centralized processing
Strategies for optimizing data loading in ETL processes include incremental loading, where only changed data is processed, and parallel processing, which distributes the workload across multiple resources for faster execution.
What are the typical trade-offs between normalization and denormalization in terms of storage and query performance?
- Both normalization and denormalization increase storage space
- Both normalization and denormalization simplify query complexity
- Denormalization increases storage space but simplifies query complexity
- Normalization reduces storage space but may increase query complexity
Normalization typically reduces storage space by eliminating redundancy but may lead to more complex queries due to the need for joins. Denormalization increases storage space by duplicating data but simplifies query complexity by reducing the need for joins.
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.
What is a distributed hash table (DHT)?
- A centralized database management system
- A decentralized key-value store
- A method for encrypting data in transit
- A protocol for network security
A distributed hash table (DHT) is a decentralized key-value store that distributes data across multiple nodes in a network. It provides a scalable and fault-tolerant solution for mapping keys to values, enabling efficient data lookup and retrieval in peer-to-peer networks. DHTs are commonly used in distributed file systems, peer-to-peer file sharing, and distributed databases.