A: Hadoop is an open-source framework for distributed storage and processing of large data sets. Its core components are HDFS (Hadoop Distributed File System) for storage and MapReduce for processing.
Q2: Explain the key features of Hadoop.
A: Key features of Hadoop include scalability, fault-tolerance, data locality, and support for parallel processing.
Q3: What is HDFS and what are its advantages?
A: HDFS is the distributed file system of Hadoop. It provides reliable and scalable storage for big data applications. Advantages of HDFS include high throughput, fault-tolerance, and support for large data sets.
Q4: What is MapReduce and how does it work?
A: MapReduce is a programming model for processing large data sets in parallel across a cluster. It works by dividing the input data into chunks, processing them independently, and then combining the results.
Q5: What is a NameNode and a DataNode?
A: NameNode is the central metadata repository in HDFS that stores information about the file system. DataNodes are the storage units in HDFS that store actual data blocks.
Q6: What is the role of YARN in Hadoop?
A: YARN (Yet Another Resource Negotiator) is the resource management framework in Hadoop. It manages cluster resources and schedules applications to run on the cluster.
Q7: Explain the concept of data locality in Hadoop.
A: Data locality refers to the principle of processing data on the same node where it is stored. It minimizes network traffic and improves overall performance.
Q8: What are the different file formats supported by Hadoop?
A: Hadoop supports various file formats, including Text, SequenceFile, Avro, Parquet, ORC, and RCFile.
Q9: How does speculative execution work in Hadoop?
A: Speculative execution is a feature in Hadoop that allows redundant tasks to be launched on different nodes to mitigate slow-running tasks and improve job completion time.
Q10: What is the purpose of a Combiner in Hadoop?
A: A Combiner is a mini-reducer that performs local aggregation of data on the map nodes before sending it to the reduce phase. It helps reduce data transfer and improves efficiency.
Q11: What is a Partitioner in Hadoop?
A: A Partitioner is responsible for dividing the intermediate key-value pairs generated by the Mapper into separate partitions, which are then processed by the reducers.
Q12: How does data compression work in Hadoop?
A: Hadoop supports data compression to reduce storage space and improve data processing efficiency. It uses codecs like Gzip, Snappy, and LZO for compression.
Q13: What is speculative execution in Hadoop MapReduce?
A: Speculative execution is a feature in Hadoop MapReduce that allows redundant tasks to be launched on different nodes to handle slow-running tasks and improve overall job completion time.
Q14: What are the different modes of Hadoop deployment?
A: Hadoop can be deployed in three modes: standalone mode, pseudo-distributed mode, and fully-distributed mode.
Q15: Explain the role of the ResourceManager in YARN.
A: The ResourceManager in YARN is responsible for allocating resources to various applications running on the cluster. It manages resources across all the nodes in the cluster.
Q16: What is a block in HDFS and what is its default size?
A: A block is the smallest unit of data storage in HDFS. The default block size in Hadoop is 128 MB.
Q17: What is the role of the JobTracker in Hadoop?
A: The JobTracker is responsible for accepting job submissions, scheduling tasks, and monitoring job execution in a Hadoop cluster. It manages the MapReduce jobs.
Q18: What is the difference between MapReduce and Spark?
A: MapReduce is a batch processing framework, whereas Spark is a fast and general-purpose cluster computing system that supports batch processing, real-time processing, and interactive queries.
Q19: What are the benefits of using Hadoop for big data processing?
A: Hadoop provides cost-effective and scalable storage and processing capabilities for big data. It enables distributed computing, fault tolerance, and parallel processing of large datasets.
Q20: How does Hadoop ensure data reliability?
A: Hadoop ensures data reliability through data replication. It stores multiple copies of data blocks across different nodes in the cluster to handle failures and ensure data availability.
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