Big Data with Hadoop

Interview Questions

Big Data refers to large and complex datasets that traditional data processing tools are unable to handle efficiently. It is characterized by the three Vs: Volume, Velocity, and Variety.
Hadoop is an open-source framework designed for distributed storage and processing of large data sets. Its core components are Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.
HDFS (Hadoop Distributed File System) is the storage layer of Hadoop, designed to store vast amounts of data across multiple nodes in a distributed manner.
Hadoop 1 had a single JobTracker for both resource management and job scheduling, while Hadoop 2 introduced YARN (Yet Another Resource Negotiator) for improved resource management.
Q.5 Explain the concept of MapReduce.
NameNode manages the metadata of the file system, while DataNode stores the actual data blocks. The NameNode keeps track of the location of data across the cluster.
Key features include scalability, fault tolerance, flexibility, and cost-effectiveness for handling large-scale data processing.
The Hadoop ecosystem consists of various tools and frameworks that complement Hadoop. Popular components include Hive, Pig, HBase, Spark, and Sqoop.
Hive is a data warehousing and SQL-like query language tool for Hadoop. It allows users to query and analyze large datasets using a language similar to SQL.
HBase is a NoSQL, non-relational, distributed database, while Hive is a data warehousing and SQL-like query language tool for Hadoop.

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