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Big Data Hadoop

Duration50 hours
Mode of TrainingOnline training

Data is generated in a variety of locations throughout the world. The data we generate can be in any format, including pictures, videos, PDFs, XLSX, and many others. It is currently unable to analyze this data with the tools available. Hadoop can process massive data sets in chunks as a Distributed File Processing System. IT Trainings Guru Training provides the greatest Real-Time Data Processing of Large Data Sets training available. You will gain practical experience with various Data Analysis Concepts such as Map Reduce by engaging with real-time data taught by real-time professionals through this Hadoop Online Training Course. Become a Certified Big Data Hadoop Professional by enrolling in the free Demo now.

Course Curriculum

    1. Why has Big Data risen to prominence so quickly?
    2. Traditional large-scale systems have limitations.
    3. In this comparison, Hadoop architecture is compared to classical architecture.
    4. Hadoops core components
    5. Getting to Know Hadoops Master-Slave Architecture
    6. The HDFS Architecture: An Overview
    7. Find out what NameNode, DataNode, and Secondary Node are.
    8. Find information about JobTracker and TaskTracker.
    9. Anatomy of HDFS Read and Write Data
    10. Standalone, single node, and multinode Hadoop deployment modes
    11. Hadoop Cluster Configuration Files
    12. Important Hadoop Web Addresses
    1. Limitations of Hadoop 1.0 Map Reduce Limitations (Mrv1 vs Mrv2)
    2. Hadoop 2.0 s timeline
    3. Architecture of HDFS 2
    4. HDFS 2 (High Availability File System)
    5. Federation HDFS 2
    6. YARN Architecture Classic vs. YARN Architecture Modern
    7. Configuring a cluster
    1. MapReduce use cases
    2. Architecture for MapReduce
    3. Understand the terms mappers and reducers.
    4. The Map Reduce Program - Anatomy
    5. Components of MapReduce - Mapper Class, Reducer Class, and Driver Code
    6. Understanding of Splits and Blocks Understanding of Combiners
    7. Format of Input/ Output
    8. Hadoop Data Types and Map Reduce API
    9. Using Writable and Comparable Writable
    10. Partitioner is a concept that includes Map Side Join, Distributed Join, Distributed Cache, and Reduce Side Join.
    1. Sqoop – How Sqoop works·
    2. Data Import/Export
    3. Architecture by Sqoop
    4. What is a Flume and How Does It Work?
    1. NoSQL (non-relational SQL) is a type of database
    2. NoSQL vs. RDBMS
    3. Observational (OLAP)
    4. When and why should you use HBase?
    5. HBase Storage/Architecture Features of HBase
    6. HBase Families HBase Data Model
    7. Master of HBase
    8. RDBMS vs. HBase
    9. Families of Columns
    10. Using the HBase API, you can access HBase data.
    11. Modes of operation
    12. HBase is being used.
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