Hadoop Interview Questions

What is Hadoop?
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware.
Explain the components of Hadoop ecosystem.
The key components include Hadoop Distributed File System (HDFS) for storage, MapReduce for processing, YARN for resource management, and various higher-level tools like Hive, Pig, HBase, and Spark.
What is HDFS?
HDFS (Hadoop Distributed File System) is a distributed file system designed to store large volumes of data reliably across multiple machines in a Hadoop cluster.
What is MapReduce?
MapReduce is a programming model and processing engine for distributed data processing in Hadoop. It involves two phases: Map phase for data processing and Reduce phase for aggregating results.
What is YARN?
YARN (Yet Another Resource Negotiator) is a resource management layer in Hadoop that allows multiple data processing engines such as MapReduce, Spark, and Tez to run on the same cluster.
What is the purpose of Hadoop Streaming?
Hadoop Streaming is a utility that allows users to create and run MapReduce jobs with any executable or script as the mapper and/or reducer, enabling integration with non-Java programs.
What is HBase?
HBase is a distributed, scalable, and NoSQL database that runs on top of Hadoop HDFS. It provides real-time read/write access to large datasets, making it suitable for random, real-time access to Big Data.
Explain the concept of data locality in Hadoop.
Data locality refers to the principle ofmoving computation to the data rather than moving data to the computation. InHadoop, tasks are scheduled on nodes that contain the data they need toprocess, minimizing network traffic and improving performance.
What are the advantages of using Hadoop?
Advantages include scalability to handle large datasets, fault tolerance, cost-effectiveness with commodity hardware, support for various data types and formats, and a vibrant open-source community.
How does Hadoop handle data redundancy and fault tolerance?
Hadoop ensures fault tolerance through data replication. It replicates data blocks across multiple nodes in the cluster, typically three times by default, ensuring that data remains available even if some nodes fail. Additionally, Hadoop can detect and recover from failures automatically.