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Hadoop Tutorial for Beginners: Learn Hadoop from Scratch
Updated: January 20, 2025 by Shubham Mishra
Apache Hadoop is an open-source software framework designed for the storage and processing of large datasets across clusters of commodity hardware. Developed by Doug Cutting and Mike Cafarella in 2005, Hadoop is licensed under the Apache License 2.0. It is one of the most popular tools for handling big data, offering scalability, fault tolerance, and high availability.
Big Data refers to extremely large datasets that cannot be stored or processed efficiently using traditional data management tools. These datasets are characterized by their volume, velocity, and variety. Hadoop is specifically designed to handle big data, making it an essential tool for modern data processing.
Big data can be categorized into three types:
The Hadoop Distributed File System (HDFS) is the storage layer of Hadoop. It divides large files into smaller blocks and distributes them across multiple servers in a cluster. This distributed storage approach ensures high availability and fault tolerance. If one server fails, the data can still be accessed from another server containing a copy of the same block.
Hadoop offers several advantages for big data processing:
To get started with Hadoop, you need to install the following tools:
core-site.xml
, hdfs-site.xml
, mapred-site.xml
, and yarn-site.xml
).start-dfs.sh
and start-yarn.sh
scripts.Hadoop is a powerful framework for processing and storing large datasets. Its scalability, fault tolerance, and cost-effectiveness make it an ideal choice for big data applications. By following this tutorial, you can get started with Hadoop and explore its capabilities in handling big data. In the next article, we will discuss the components and architecture of Hadoop in detail.
Practice coding regularly, work on small projects, and explore Hadoop's extensive ecosystem to become proficient in big data processing. Best of luck on your coding journey!