Data Science: Hadoop (Definitions)

"An Apache-managed software framework derived from MapReduce and Bigtable. Hadoop allows applications based on MapReduce to run on large clusters of commodity hardware. Hadoop is designed to parallelize data processing across computing nodes to speed computations and hide latency. Two major components of Hadoop exist: a massively scalable distributed file system that can support petabytes of data and a massively scalable MapReduce engine that computes results in batch." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"An open-source software platform developed by Apache Software Foundation for data-intensive applications where the data are often widely distributed across different hardware systems and geographical locations." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"Technology designed to house Big Data; a framework for managing data" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"an Apache-managed software framework derived from MapReduce. Big Table Hadoop enables applications based on MapReduce to run on large clusters of commodity hardware. Hadoop is designed to parallelize data processing across computing nodes to speed up computations and hide latency. The two major components of Hadoop are a massively scalable distributed file system that can support petabytes of data and a massively scalable MapReduce engine that computes results in batch." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"An open-source framework that is built to process and store huge amounts of data across a distributed file system." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"Open-source software framework for distributed storage and distributed processing of Big Data on clusters of commodity hardware." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"A batch processing infrastructure that stores fi les and distributes work across a group of servers. The infrastructure is composed of HDFS and MapReduce components. Hadoop is an open source software platform designed to store and process quantities of data that are too large for just one particular device or server. Hadoop’s strength lies in its ability to scale across thousands of commodity servers that don’t share memory or disk space." (Benoy Antony et al, "Professional Hadoop®", 2016)

"Apache Hadoop is an open-source framework for processing large volume of data in a clustered environment. It uses simple MapReduce programming model for reliable, scalable and distributed computing. The storage and computation both are distributed in this framework." (Kaushik Pal, 2016)

"A framework that allow for the distributed processing for large datasets." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

 "Hadoop is an open source implementation of the MapReduce paper. Initially, Hadoop required that the map, reduce, and any custom format readers be implemented and deployed to the cluster. Eventually, higher level abstractions were developed, like Apache Hive and Apache Pig." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"A batch processing infrastructure that stores files and distributes work across a group of servers." (Oracle)

"an open-source framework that is built to enable the process and storage of big data across a distributed file system." (Analytics Insight)

"Apache Hadoop is an open-source, Java-based software platform that manages data processing and storage for big data applications. Hadoop works by distributing large data sets and analytics jobs across nodes in a computing cluster, breaking them down into smaller workloads that can be run in parallel. Hadoop can process both structured and unstructured data, and scale up reliably from a single server to thousands of machines." (Databricks) [source]

"Hadoop is an open source software framework for storing and processing large volumes of distributed data. It provides a set of instructions that organizes and processes data on many servers rather than from a centralized management nexus." (Informatica) [source]