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Apache Hadoop (/ h ə ˈ d uː p /) is a collection of open-source software utilities for reliable, scalable, distributed computing.It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.
Unlike other operating systems, Linux and Unix allow any file system regardless of the medium it is stored on, whether that medium is a magnetic disk, an optical disk (CD, DVD, etc.), a USB flash memory key, or even contained within a file located on another file system.
Two of the most widely used distributed file systems (DFS) of this type are the Google File System (GFS) and the Hadoop Distributed File System (HDFS). The file systems of both are implemented by user level processes running on top of a standard operating system (Linux in the case of GFS). [8]
Install to a flash memory card for portability to other machines. Run on Windows, Linux and Mac OS X. View the HTML for any wiki page. Search for any page by title using a Wikipedia-like Search box. Browse pages by alphabetical order using Special:AllPages. Find a word on a page. Access a history of viewed pages. Bookmark your favorite pages.
In a typical Lustre installation on a Linux client, a Lustre filesystem driver module is loaded into the kernel and the filesystem is mounted like any other local or network filesystem. Client applications see a single, unified filesystem even though it may be composed of tens to thousands of individual servers and MDT/OST filesystems.
Apache Pig [1] is a high-level platform for creating programs that run on Apache Hadoop. The language for this platform is called Pig Latin. [1] Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark. [2]
Avro is a row-oriented remote procedure call and data serialization framework developed within Apache's Hadoop project. It uses JSON for defining data types and protocols, and serializes data in a compact binary format.
Apache Kudu is a free and open source column-oriented data store of the Apache Hadoop ecosystem. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides completeness to Hadoop's storage layer to enable fast analytics on fast data. [3]