Apache Hadoop is an open-source framework designed for distributed storage and processing of large sets of data across clusters of computers. It provides a scalable and reliable platform for storing, managing, and analyzing vast amounts of data, including structured, semi-structured, and unstructured data.

Key components of the Hadoop ecosystem include:

• Hadoop Distributed File System (HDFS): This is the primary storage system of Hadoop, designed to store data across multiple machines in a distributed manner. It offers high fault tolerance and data replication to ensure data durability.
• MapReduce: A programming model and processing engine used to process and analyze large datasets in parallel. It divides tasks into smaller sub-tasks that can be processed independently on different nodes of the cluster.
• YARN (Yet Another Resource Negotiator): YARN is a resource management and job scheduling component that allows multiple applications to share cluster resources efficiently.
• Hadoop Common: Provides libraries and utilities that are used by other Hadoop modules.
• Hive: A data warehousing and SQL-like query language for analyzing and querying large datasets stored in Hadoop. It allows users to write queries without having to know the details of the underlying MapReduce jobs.
• Pig: A high-level platform for creating MapReduce programs used for data analysis. It provides a scripting language called Pig Latin for expressing data transformations and analysis.
• HBase: A NoSQL database that provides real-time read and write access to large datasets. It is suitable for applications that require random, real-time access to data.
• Spark: Although not originally part of the Hadoop project, Apache Spark is often used alongside Hadoop. It is an open-source data processing and analytics engine that offers faster processing speeds and a more flexible programming model than MapReduce.
• Ambari: A management and monitoring tool for Hadoop clusters. It provides an intuitive web interface for managing and configuring Hadoop components.

Hadoop’s architecture allows organizations to store and process massive amounts of data economically and efficiently. It is particularly well-suited for big data applications where traditional databases may become impractical or cost-prohibitive. Hadoop has found applications in various industries, including finance, healthcare, e-commerce, social media, and more, enabling organizations to gain valuable insights from their data.

References

Apache Hadoop Official Website: The official source for information, documentation, and downloads related to Apache Hadoop. Website: https://hadoop.apache.org/

Hadoop Wiki: A collaborative space with a wealth of information about Hadoop, including tutorials, use cases, and best practices. Website: https://cwiki.apache.org/confluence/display/HADOOP2/

Hortonworks Documentation: Comprehensive documentation and resources related to Hadoop, including installation guides, tutorials, and use cases. Website: https://docs.cloudera.com/HDPDocuments/HDP3/

Cloudera Blog: Cloudera provides insights, tutorials, and articles about Hadoop, big data, and related technologies. Website: https://blog.cloudera.com/

MapR Blog: MapR’s blog offers articles and tutorials on Hadoop, real-time data processing, and analytics. Website: https://www.mapr.com/blog/

DataFlair Hadoop Tutorials: A collection of Hadoop tutorials covering various topics and components of the Hadoop ecosystem. Website: https://data-flair.training/blogs/category/hadoop/

TutorialsPoint Hadoop Tutorial: A beginner-friendly tutorial covering Hadoop concepts, installation, and usage. Website: https://www.tutorialspoint.com/hadoop/index.htm

Edureka Hadoop Tutorial: Video-based tutorials covering Hadoop concepts, installation, and practical examples. Website: https://www.edureka.co/blog/hadoop-tutorial/