12. Master Hadoop 1.X Architecture & HDFS Framework | Data Raj Academy

Deep dive into Hadoop 1.X architecture and HDFS framework with expert trainer Koka Venu. Perfect for aspiring data engineers! 🚀

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This is a video on Hadoop Architecture - Hadoop 1.X architecture, HDFS Framework Offered by - Data Raj Academy Name of the trainer - Koka Venu ----------------------------------------------------------------------------------------------------------------------------------------------------- In the earlier session, we discussed about Hadoop 1.X architecture by taking an example of a simple and small cluster composing of 5 nodes. In the real world, we can find Hadoop clusters with a varying number of nodes. It could range from a few tens of nodes in a development cluster of smaller organizations to thousands of nodes in a production cluster deployed by bigger organizations like Facebook or Google. We have discussed how Hadoop can be understood as an operating system of a cluster. We have discussed what it means to run a Hadoop cluster, the various processes and daemons related to Hadoop, the master-slave architecture adopted by Hadoop etc. We also classified nodes into two categories : Master nodes and slave nodes. We will look into an another category of nodes called Client nodes or Edge nodes, their role and importance. The goal of these couple of videos is to get few basics right so that we can thoroughly understand and enjoy every step in the installation process. So, in line with our goal, we will extend our discussion on Hadoop processes, the various server roles, and their hardware requirements. We will also look into the various possible configurations of Hadoop cluster. People who are aware of Hadoop 1.X architecture may skip this session, but if you stay back I am sure you will find it worthwhile. This session will give us a thorough understanding of Hadoop 1.X architecture and how distributed components play together. With this knowledge, we will explore what are Hadoop distributions in the next session. -----------------------------------------------------------------------------------------------------------------------------------------------------

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30:58

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May 3, 2018

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