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Big Data Analytics: Distributed Storage & Distributed File Systems

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Big Data Analytics: Distributed Storage & Distributed File Systems

Course Overview
This study material covers the essential concepts of Distributed Storage and Distributed File Systems (DFS), key components for managing large-scale data across multiple nodes in big data environments. Learn about popular distributed file systems like Google File System (GFS) and Hadoop Distributed File System (HDFS), and understand their architecture, replication management, and file health monitoring.

Key Topics Covered:

  • Why Do We Need a Distributed File System?: Explore the necessity of a distributed file system in handling massive datasets spread across multiple machines. Learn how DFS supports fault tolerance, scalability, and efficient data retrieval in Big Data Analytics.

  • What is a Distributed File System?:

    • Examples of DFS: Introduction to well-known DFS solutions, such as Google File System (GFS) and Hadoop Distributed File System (HDFS).
    • Components of DFS: Understand the key components of a distributed file system, including data nodes, name nodes, and metadata management.
    • Storing Files: Learn how files are split into blocks and distributed across nodes, ensuring redundancy and fast access.
    • Considerations for DFS: Explore important factors like fault tolerance, load balancing, latency, and throughput when working with distributed storage systems.
    • Replication Management: Learn how DFS handles data replication to ensure data availability even in the case of node failures.

  • GFS and HDFS:

    • GFS vs. HDFS: Compare the Google File System (GFS) and the Hadoop Distributed File System (HDFS), their similarities and differences in terms of architecture, performance, and use cases.
    • Google File System: Understand the architecture and design principles behind GFS, the foundation for many modern distributed storage systems.

  • Hadoop Distributed File System (HDFS):

    • Hadoop Overall Architecture: A deep dive into the architecture of Hadoop, including its NameNode, DataNodes, and how it manages large datasets across clusters.
    • Hadoop HDFS Setup: Step-by-step guide on setting up HDFS for big data projects.
    • HDFS Web Interface: Learn how to use Hadoop’s web-based interface to monitor system health, inspect file status, and manage storage nodes.
    • HDFS Filesystem Interface: Explore how to interact with HDFS using the command line to upload, download, and inspect files.
    • Inspecting File Health in HDFS: Learn how to check the health and replication status of files stored in HDFS to ensure data integrity.

Why Choose This Material?

  • Comprehensive coverage of distributed storage systems with a focus on HDFS and GFS.
  • Practical insights into setting up, managing, and monitoring Hadoop HDFS for large-scale data storage.
  • Perfect for students and professionals looking to understand the backbone of distributed storage in Big Data Analytics.

This material is ideal for students, data engineers, and big data professionals seeking to build, manage, and scale distributed storage systems in their big data projects.

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