A ________ is a database design pattern that stores data in columns rather than rows, allowing for faster data loading and retrieval.
- Columnar Store
- Document Store
- Graph Database
- Key-Value Store
A columnar store is a database design pattern that stores data in columns rather than rows, allowing for faster data loading and retrieval, especially when dealing with analytical queries that involve aggregations or scanning large datasets.
What are the key components of a data security policy?
- Access controls, encryption, and data backups
- Data analysis, visualization, and reporting
- Networking protocols, routing, and switching
- Software development, testing, and deployment
A data security policy typically includes key components such as access controls, encryption mechanisms, and data backup procedures. Access controls regulate who can access data and under what circumstances, while encryption ensures that data remains confidential and secure during storage and transmission. Data backups are essential for recovering lost or corrupted data in the event of a security breach or system failure. Together, these components help mitigate risks and protect against unauthorized access and data breaches.
What role does metadata play in the ETL process?
- Analyzing data patterns, Predicting data trends, Forecasting data usage, Optimizing data processing
- Classifying data types, Indexing data attributes, Archiving data records, Versioning data schemas
- Describing data structures, Documenting data lineage, Defining data relationships, Capturing data transformations
- Monitoring data performance, Managing data storage, Governing data access, Securing data transmission
Metadata in the ETL process plays a crucial role in describing data structures, documenting lineage, defining relationships, and capturing transformations, facilitating efficient data management and governance.
What are some common challenges associated with data extraction from heterogeneous data sources?
- All of the above
- Data inconsistency
- Data security concerns
- Integration complexity
Common challenges in extracting data from heterogeneous sources include data inconsistency, security concerns, and integration complexity due to differences in formats and structures.
Which streaming processing architecture provides fault tolerance and guarantees exactly-once processing semantics?
- Amazon Kinesis
- Apache Flink
- Apache Kafka
- Apache Spark
Apache Flink is a streaming processing framework that provides fault tolerance and guarantees exactly-once processing semantics. It achieves fault tolerance through its distributed snapshot mechanism, which periodically checkpoints the state of the stream processing application. Additionally, Flink's transactional processing capabilities ensure exactly-once semantics by managing state updates and output operations atomically.
When dealing with large datasets, which data loading technique is preferred for its efficiency?
- Bulk loading
- Random loading
- Sequential loading
- Serial loading
Bulk loading is preferred for its efficiency when dealing with large datasets. It involves loading data in large batches, which reduces overhead and improves performance compared to other loading techniques.
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.
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.
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.
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.
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 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.