Which component in Hadoop is primarily responsible for managing security policies?
- DataNode
- JobTracker
- NameNode
- ResourceManager
The NameNode in Hadoop is primarily responsible for managing security policies. It stores metadata and information about file permissions, ensuring secure access to data stored in the Hadoop Distributed File System (HDFS).
In a Hadoop application dealing with multimedia files, what considerations should be made for InputFormat and compression?
- CombineFileInputFormat with Bzip2
- Custom InputFormat with LZO
- KeyValueTextInputFormat with Snappy
- TextInputFormat with Gzip
In a Hadoop application handling multimedia files, using CombineFileInputFormat with Bzip2 compression is beneficial. This allows processing multiple small files as a single split, reducing overhead, and Bzip2 is suitable for compressing multimedia files.
Apache Spark's ____ abstraction provides an efficient way of handling distributed data across nodes.
- DataFrame
- RDD (Resilient Distributed Dataset)
- SparkContext
- SparkSQL
Apache Spark's RDD (Resilient Distributed Dataset) abstraction is a fundamental data structure that provides fault-tolerant distributed processing of data across nodes. It allows efficient data handling and transformation in a parallel and resilient manner.
What is the primary role of Apache Pig in Hadoop for data transformation?
- Data Processing
- Data Storage
- Data Transformation
- Query Language
Apache Pig is a platform for processing and analyzing large datasets in Hadoop. Its primary role is data transformation, providing a high-level scripting language, Pig Latin, to express data transformation tasks easily. Pig converts these scripts into a series of MapReduce jobs for execution.
Hadoop's ____ mechanism allows for automated recovery of data in case of a DataNode failure.
- Recovery
- Redundancy
- Replication
- Resilience
Hadoop's Replication mechanism allows for automated recovery of data in case of a DataNode failure. It ensures that multiple copies of data blocks are maintained across the cluster, providing fault tolerance and reliability. If a DataNode becomes unavailable, Hadoop can retrieve the data from other replicated copies.
What advanced technique in Hadoop data pipelines is used for processing large datasets in near real-time?
- Apache Flink
- Apache Spark
- MapReduce
- Pig Latin
Apache Spark is an advanced technique in Hadoop data pipelines used for processing large datasets in near real-time. It enables in-memory data processing, iterative algorithms, and interactive queries, making it suitable for a wide range of real-time analytics scenarios.
In a situation with fluctuating data loads, how does YARN's resource management adapt to ensure efficient processing?
- Capacity Scheduler
- Fair Scheduler
- Queue Prioritization
- Resource Preemption
YARN's resource management adapts to fluctuating data loads through Resource Preemption. If a high-priority application requires resources, YARN can preempt resources from lower-priority applications, ensuring that critical workloads receive the necessary resources for efficient processing.
The practice of ____ is important for debugging and maintaining Hadoop applications.
- Load Testing
- Regression Testing
- Stress Testing
- Unit Testing
The practice of unit testing is important for debugging and maintaining Hadoop applications. Unit tests focus on validating the functionality of individual components or modules, ensuring that each part of the application works as intended. This is essential for identifying and fixing bugs during development.
Explain the concept of co-processors in HBase and their use case.
- Custom Filters
- Extending Server Functionality
- In-memory Processing
- Parallel Computing
Co-processors in HBase allow users to extend the functionality of HBase servers by running custom code alongside the normal processing. This can be used for tasks like custom filtering, in-memory processing, and parallel computing, enhancing the capabilities of HBase for specific use cases.
In Flume, how are complex data flows managed for efficiency and scalability?
- Multiplexing
- Pipelining
- Streamlining
- Topology
Complex data flows in Apache Flume are managed using a topology-based approach. The topology allows the definition of a flow's structure, components, and their interconnections, ensuring efficiency and scalability in handling intricate data processing tasks.