UnderStanding Hadoop


            UnderStanding Hadoop


It is designed to scale up from a single server to thousands of machines, each offering local computation and storage.

Here’s an understanding of its main components:

  1. Hadoop Common: These are the Java libraries and utilities required by other Hadoop modules. They provide filesystem and OS level abstractions and contain the necessary Java files and scripts required to start Hadoop.

  2. Hadoop Distributed File System (HDFS): A distributed file system that provides high-throughput access to application data. It’s designed to store a large amount of data and provide access to it across the network.

  3. Hadoop YARN: This is a framework for job scheduling and cluster resource management. YARN stands for Yet Another Resource Negotiator.

  4. Hadoop MapReduce: This is a YARN-based system for parallel processing of large data sets. It divides the task into small parts and assigns them to many computers. The ‘Map’ job breaks down tasks, and the ‘Reduce’ job takes the output from the map and combines those data tuples into smaller sets.

  5. Other Ecosystems: Besides these, there are other tools and add-ons such as Hive, Pig, HBase, ZooKeeper, etc., that integrate with Hadoop to provide additional functionalities like data warehousing, data querying, etc.

Advantages of Hadoop:

  • Scalable: Hadoop can handle thousands of terabytes of data and thousands of nodes in a single cluster.
  • Fault-Tolerant: Data is replicated across different nodes, so if one node fails, data can be retrieved from another.
  • Cost-Effective: It can run on commodity hardware, saving costs.
  • Flexible: It can process various forms of structured and unstructured data.


  • Complexity: Setting up and maintaining a Hadoop cluster can be complicated.
  • Latency: Not suitable for real-time data processing as it can have high latency.

Use Cases:

  • Data Processing & Analysis: Used by companies like Facebook, Yahoo, and eBay to analyze user data and improve customer experience.
  • Data Storage: Storing large datasets without the need for an excessive amount of expensive hardware.
  • Data Search: Powers search algorithms for various search engines.

Hadoop has become an essential tool for big data analytics and management. Its open-source nature and strong community support make it an attractive option for organizations looking to handle vast amounts of data efficiently.

Hadoop Training Demo Day 1 Video:

You can find more information about Hadoop Training in this Hadoop Docs Link



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