Answer: Big Data refers to the vast volume of structured and unstructured data that cannot be easily managed, processed, or analyzed using traditional data processing techniques.
Answer: Hadoop is an open-source framework designed to store and process large datasets across distributed computing clusters. It consists of the Hadoop Distributed File System (HDFS) and the MapReduce processing model.
Answer: HDFS is the distributed file system used by Hadoop. It provides high-throughput access to data across multiple machines, enabling reliable and scalable storage for Big Data.
Answer: The key components of the Hadoop ecosystem include HDFS (Hadoop Distributed File System), MapReduce, YARN (Yet Another Resource Negotiator), Hive, Pig, HBase, Spark, and others.
Answer: MapReduce is a programming model and processing framework in Hadoop that allows distributed processing of large datasets across a cluster. It divides the processing into two phases: Map and Reduce.
Answer: YARN (Yet Another Resource Negotiator) is the resource management framework in Hadoop. It manages and allocates resources to various applications running on the Hadoop cluster.
Answer: Hadoop is a batch processing framework, while Spark is an in-memory, distributed computing framework. Spark provides faster data processing and supports real-time analytics.
Answer: Data locality refers to the principle of processing data on the same node where it is stored in Hadoop. It reduces network congestion and improves data processing performance.
Answer: Hive is a data warehousing tool in Hadoop that provides a SQL-like interface for querying and analyzing data stored in Hadoop. It converts queries into MapReduce jobs for execution.
Answer: Pig is a high-level scripting language in Hadoop that simplifies data processing tasks. It provides a platform for executing data transformations and analysis using a scripting language called Pig Latin.
Answer: HBase is a distributed, scalable, and column-oriented NoSQL database in Hadoop. It provides real-time read and write access to Big Data stored in HDFS.
Answer: Data partitioning is the process of dividing data into smaller, manageable chunks across multiple nodes in a Hadoop cluster. It enables parallel processing and improves data processing efficiency.
Answer: Sqoop is a tool in Hadoop used for importing and exporting data between Hadoop and relational databases. It simplifies the transfer of data between Hadoop and structured data sources.
Answer: Structured data refers to data that is organized and follows a predefined schema, such as data stored in relational databases. Unstructured data, on the other hand, does not have a predefined structure and includes text, images, videos, social media posts, etc.
Answer: Apache Spark is a fast and general-purpose distributed computing system that provides in-memory processing capabilities for Big Data. It supports real-time streaming, machine learning, graph processing, and more.
Answer: Data serialization is the process of converting complex data structures into a format that can be stored or transmitted. In Hadoop, data serialization is used to store and process data efficiently in a distributed environment.
Answer: Data replication in Hadoop involves creating multiple copies of data blocks and distributing them across different nodes in the cluster. It provides fault tolerance and ensures data availability in case of node failures.
Answer: Some challenges of working with Big Data include data storage and management, data integration, data quality, data privacy and security, processing speed, and scalability.
Answer: Hadoop ensures fault tolerance through data replication. It maintains multiple copies of data blocks across different nodes in the cluster. If a node fails, the data can be retrieved from the replicated copies.
Answer: The NameNode is the central component of HDFS in Hadoop. It manages the file system namespace, stores metadata, and coordinates data access and storage across the cluster.
Answer: Hadoop handles data processing failures by automatically reassigning failed tasks to other available nodes in the cluster. It ensures the completion of data processing tasks even in the presence of node failures.
Answer: A DataNode is responsible for storing and retrieving data blocks in HDFS, while a Task Tracker is responsible for executing MapReduce tasks in Hadoop.
Answer: Data compression is the process of reducing the size of data to save storage space and improve data processing efficiency. Hadoop supports various compression codecs such as Grip, Snappy, and LZO.
Answer: The Job Tracker is responsible for coordinating and managing MapReduce jobs in Hadoop. It assigns tasks to Task Trackers, monitors their progress, and handles job scheduling and fault tolerance.
Answer: Hadoop supports scalability by allowing the addition of more nodes to the cluster as the data and processing requirements grow. It distributes data and processing tasks across the cluster, enabling horizontal scalability.