What is the function of a Combiner in the MapReduce process?
- Data Compression
- Intermediate Data Filtering
- Result Aggregation
- Task Synchronization
The function of a Combiner in MapReduce is result aggregation. It combines (or aggregates) the intermediate output generated by the Mapper before sending it to the Reducer. This helps in reducing the volume of data transferred over the network and improves overall processing efficiency.
What is the primary role of Hadoop's HDFS snapshots in data recovery?
- Data compression
- Load balancing
- Point-in-time recovery
- Real-time processing
Hadoop's HDFS snapshots play a crucial role in point-in-time recovery. They capture the state of the file system at a specific point, allowing users to revert to that state in case of data corruption or accidental deletion. This feature enhances data recovery capabilities in Hadoop.
Hadoop's ____ feature allows automatic failover of the NameNode service in case of a crash.
- Fault Tolerance
- High Availability
- Load Balancing
- Scalability
Hadoop's High Availability feature allows automatic failover of the NameNode service in case of a crash. This ensures continuous operation of the Hadoop cluster even in the face of a NameNode failure, enhancing the reliability of the system.
In Apache Pig, which operation is used for joining two datasets?
- GROUP
- JOIN
- MERGE
- UNION
The operation used for joining two datasets in Apache Pig is the JOIN operation. It enables the combination of records from two or more datasets based on a specified condition, facilitating the merging of related information from different sources.
For a use case requiring high throughput and low latency data access, how would you configure HBase?
- Adjust Write Ahead Log (WAL) settings
- Enable Compression
- Implement In-Memory Compaction
- Increase Block Size
In scenarios requiring high throughput and low latency, configuring HBase for in-memory compaction can be beneficial. This involves keeping more data in memory, reducing the need for disk I/O and enhancing data access speed. It's particularly effective for read-heavy workloads with a focus on performance.
For a use case involving time-sensitive data analysis, what Hive capability would you leverage to ensure quick query response times?
- Cost-Based Optimization
- LLAP (Live Long and Process)
- Partitioning
- Tez Execution Engine
LLAP (Live Long and Process) in Hive is designed for low-latency query processing. It allows long-running daemons to keep processing data, providing quick response times for time-sensitive data analysis scenarios. LLAP maintains cached data for faster query execution.
____ in HBase refers to the technique of storing the same data in different formats for performance optimization.
- Data Compression
- Data Encryption
- Data Serialization
- Data Sharding
In HBase, data compression refers to the technique of storing the same data in different formats for performance optimization. It reduces storage space and improves read and write performance by compressing the data before storage.
What mechanism does YARN use to ensure high availability and fault tolerance?
- Active-Standby Configuration
- Container Resilience
- Load Balancing
- Speculative Execution
YARN ensures high availability and fault tolerance through an Active-Standby configuration. In this setup, there are primary and secondary ResourceManager nodes. If the primary fails, the secondary takes over, ensuring continuous operation and fault tolerance.
____ is an essential Hadoop ecosystem component for real-time processing and analysis of streaming data.
- Flume
- HBase
- Kafka
- Spark
Kafka is an essential Hadoop ecosystem component for real-time processing and analysis of streaming data. It acts as a distributed publish-subscribe messaging system, providing high-throughput, fault tolerance, and scalability for handling real-time data streams.
For a Hadoop data pipeline focusing on real-time data processing, which framework is most appropriate?
- Apache HBase
- Apache Hive
- Apache Kafka
- Apache Pig
For real-time data processing in Hadoop, Apache Kafka is the most suitable framework. Kafka is a distributed streaming platform that allows for the ingestion and processing of real-time data streams. It provides high-throughput, fault tolerance, and scalability, making it ideal for building real-time data pipelines.