How does Apache Flume ensure data reliability during transfer to HDFS?
- Acknowledgment Mechanism
- Data Compression
- Data Encryption
- Load Balancing
Apache Flume ensures data reliability during transfer to HDFS through an acknowledgment mechanism. This mechanism involves confirming the successful receipt of data events, ensuring that no data is lost during the transfer process. It contributes to the reliability and integrity of the data being ingested into Hadoop.
For a use case requiring the merging of streaming and batch data, how can Apache Pig be utilized?
- Implement a custom MapReduce job
- Use Apache Flink
- Use Pig Streaming
- Utilize Apache Kafka
Apache Pig can be utilized for merging streaming and batch data by using Pig Streaming. This feature enables the integration of real-time data processing with batch processing in a seamless manner, making it suitable for scenarios that involve both streaming and batch data sources.
In a scenario where a Hadoop cluster must handle large-scale data processing, what key factor should be considered for DataNode configuration?
- CPU Performance
- Memory Allocation
- Network Bandwidth
- Storage Capacity
In a scenario of large-scale data processing, the key factor to consider for DataNode configuration is Network Bandwidth. Efficient data transfer between DataNodes is crucial to prevent bottlenecks and ensure timely processing of large volumes of data.
____ is a critical Sqoop configuration for balancing network load and performance during data transfer.
- --connectivity-factor
- --data-balance
- --network-throttle
- --num-mappers
--network-throttle is a critical Sqoop configuration that helps balance network load and performance during data transfer. It allows users to control the rate at which data is transferred, optimizing the data transfer process.
In Flume, ____ are used for transforming incoming events before they are stored in the destination.
- Channels
- Interceptors
- Sinks
- Sources
In Flume, Interceptors are used for transforming incoming events before they are stored in the destination. They allow users to modify or augment the events as they flow through the Flume pipeline, providing flexibility in data processing.
For a Hadoop cluster intended for high-throughput streaming data, what capacity planning considerations are essential?
- Data Replication
- Disk I/O
- Memory Allocation
- Network Bandwidth
In a high-throughput streaming data scenario, network bandwidth is essential in capacity planning. Streaming data applications rely on fast data movement, and sufficient network capacity ensures timely data transmission within the cluster.
In the context of Hadoop, ____ plays a significant role in network capacity planning.
- HDFS
- MapReduce
- YARN
- ZooKeeper
In the context of Hadoop, YARN (Yet Another Resource Negotiator) plays a significant role in network capacity planning. YARN manages resources and schedules tasks across the cluster, optimizing the utilization of resources and enhancing network efficiency.
For disaster recovery, Hadoop clusters often use ____ replication across geographically dispersed data centers.
- Block
- Cross-Datacenter
- Data-Local
- Rack-Local
For disaster recovery, Hadoop clusters often use Cross-Datacenter replication. This involves replicating data across different geographical data centers, ensuring data availability and resilience in case of a disaster or data center failure.
How can a Hadoop administrator resolve a 'Data Skew' issue in a MapReduce job?
- Combiner Usage
- Custom Partitioning
- Data Replication
- Dynamic Input Splitting
A Hadoop administrator can resolve a 'Data Skew' issue in a MapReduce job by using dynamic input splitting. This involves dynamically adjusting the input splits based on the size of the data to ensure that each mapper gets a balanced workload, mitigating the impact of data skew and improving overall job performance.
In optimizing data processing, Hadoop Streaming API's compatibility with ____ plays a crucial role in handling large datasets.
- Apache Hive
- Apache Impala
- Apache Kafka
- Apache Pig
Hadoop Streaming API's compatibility with Apache Pig is crucial in optimizing data processing, especially for handling large datasets. Pig allows developers to express data transformations using a high-level scripting language, making it easier to work with complex data processing tasks.