Facebook Tackles Big Data With Hadoop: A Comprehensive Guide
Facebook Tackles Big Data with Hadoop: A Comprehensive Guide
As Facebook tackles big data with Hadoop, it is important for students to understand the advantages of this powerful data processing tool. Hadoop is an open source software framework that is used for distributed storage and distributed processing of big data on clusters of computers. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
What is Hadoop?
Hadoop is an open source framework that enables distributed storage and processing of large data sets across multiple computers. It is used to store, manage, and process large amounts of data that are too big to be handled by a single server. Hadoop is comprised of four main components: the Hadoop Distributed File System (HDFS), MapReduce, YARN, and the Hadoop Common. Each component plays an important role in the Hadoop framework.
Hadoop Distributed File System (HDFS)
HDFS is a distributed file system that stores data across multiple machines. It provides high aggregate bandwidth across the cluster, allowing for parallel processing of large data sets. HDFS is designed to scale from single servers to thousands of machines, each with its own local storage. It is also fault tolerant and highly available, meaning that it can tolerate the failure of any individual machine without affecting the data stored on the cluster.
MapReduce is a programming model that enables data processing on large clusters of computers. It is used to process and analyze large data sets in a distributed fashion. MapReduce is designed to be parallelizable, meaning that it can take advantage of the computing power of a cluster of machines. MapReduce is often used to process large amounts of data, such as web logs, social media data, or scientific data.
YARN (Yet Another Resource Negotiator) is a resource management platform that enables applications to run in a distributed environment. It is responsible for allocating resources such as CPU and memory to applications on a cluster. YARN also provides a scheduling platform that can be used to manage applications on the cluster.
Hadoop Common is the collection of utilities and libraries that support the other Hadoop components. It contains the Java Archive (JAR) files and scripts necessary to start Hadoop. It also contains the necessary libraries and configuration files for running Hadoop on a cluster.
Why Use Hadoop?
Hadoop is a powerful data processing tool that enables organizations to tackle large data sets. It is designed for scalability and fault tolerance, meaning that it can handle an increase in data size without sacrificing performance. Hadoop also provides a distributed processing platform, allowing organizations to process data in parallel and increase their computing power. Furthermore, Hadoop is an open source framework, meaning that it is free to use and can be customized to meet specific needs.
Dated : 01-Feb-2023