Course Details

Our intelligently designed course on big data helps you master big data concepts,methodologies, and tools. Join our Summer training in Big Data Hadoop course and be prepared to start a great career as a big data developer! After completing our course, you will get a detailed insight into big data, Hadoop, several Hadoop components, parallel processing, spark applications, functional programming, and knowledge about different data analysis tools that would assure you’ll have the best understanding of big data, and Hadoop.

Each module of our course will make your learning experience easier. And another unique thing about Euphoria GenX is we don’t focus on theoretical knowledge only. Our experts think practical experience matters more than this.

That’s why to make our students industry-prepared, we offer an opportunity to our students to work on real-time projects! Our experts assure that each student can get the opportunity to go through an immersive learning experience and can start a promising career using big data and Hadoop successfully. To learn big data and Hadoop, get in touch with the leading career solution provider in Kolkata now!

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    Big Data Hadoop

    Big Data Hadoop

    • Module 1:Introduction

      • What is Big Data
      • Necessity of Big Data in the industry
      • Paradigm shift - why the industry is shifting to Big Data tools
      • Different dimensions of Big Data Data explosion in the industry
      • Various implementations of Big Data
      • Different technologies to handle Big Data Traditional systems and associated problems
      • Future of Big Data in the IT industry

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    • Module 2: Demystifying Hadoop

      • Why Hadoop is at the heart of every Big Data solution
      • Introduction to the Hadoop framework
      • Hadoop architecture and design principles
      • Ingredients of Hadoop
      • Hadoop characteristics and data-flow
      • Components of the Hadoop ecosystem
      • Hadoop Flavors – Apache
      • Cloudera
      • Hortonworks
      • and more

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    • Module 3: Setup and Installation of Hadoop

      • Environment setup and pre-requisites Installation and configuration of Hadoop
      • Working with Hadoop in pseudo-distributed mode
      • Troubleshooting encountered problems
      • Setup and Installation of Hadoop multi-node cluster
      • Hadoop environment setup on the cloud (Amazon cloud)
      • Installation of Hadoop pre-requisites on all nodes
      • Configuration of masters and slaves on the cluster
      • Playing with Hadoop in distributed mode

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    • Module 4: HDFS – The Storage Layer

      • What is HDFS (Hadoop Distributed File System)
      • HDFS daemons and architecture
      • HDFS data flow and storage mechanism
      • Hadoop HDFS characteristics and design principles
      • Responsibility of HDFS Master – NameNode
      • Storage mechanism of Hadoop meta-data
      • Work of HDFS Slaves – DataNodes
      • Data Blocks and distributed storage Replication of blocks
      • reliability
      • and high availability
      • Rack-awareness
      • scalability
      • and other features
      • Different HDFS APIs and terminologies
      • Commissioning of nodes and addition of more nodes
      • Expanding clusters in real-time Hadoop
      • HDFS Web UI and HDFS explorer
      • HDFS best practices and hardware discussion

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    • Module 5: A Deep Dive into MapReduce

      • What is MapReduce
      • the processing layer of Hadoop
      • The need for a distributed processing framework
      • Issues before MapReduce and its evolution
      • List processing concepts
      • Components of MapReduce – Mapper and Reducer MapReduce terminologies- keys
      • values
      • lists
      • and more Hadoop
      • MapReduce execution flow
      • Mapping and reducing data based on keys
      • MapReduce word-count example to understand the flow
      • Execution of Map and Reduce together
      • Controlling the flow of mappers and reducers
      • Optimization of MapReduce Jobs
      • Fault-tolerance and data locality
      • Working with map-only jobs
      • Introduction to Combiners inMapReduce
      • How MR jobs can be optimized using combiners

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    • Module 6: MapReduce – Advanced Concepts

      • Anatomy of MapReduce
      • Hadoop MapReduce data types
      • Developing custom data types using Writable & WritableComparable
      • Input Format in MapReduce
      • InputSplit as a unit of work
      • How Partitioners partition data
      • Customization of Record Reader
      • Moving data from mapper to reducer – shuffling & sorting
      • Distributed cache and job chaining
      • Different Hadoop case-studies to customize each component
      • Job scheduling in MapReduce

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    • Module 7: Hive – Data Analysis Tool

      • The need for an adhoc SQL based solution – Apache Hive
      • Introduction to and architecture of HadoopHive
      • Playing with the Hive shell and running HQL queries
      • Hive DDL and DML operations
      • Hive execution flow
      • Schema design and other Hive operations
      • Schema-on-Read vs Schema-on-Write in Hive Meta-store management and the need for RDBMS
      • Limitations of the default meta-store
      • Using SerDe to handle different types of data
      • Optimization of performance using partitioning
      • Different Hive applications and use cases

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    • Module 8: Pig – Data Analysis Tool

      • The need for a high level query language - Apache Pig
      • How Pig complements Hadoop with a scripting language
      • What is Pig Pig execution flow
      • Different Pig operations like filter and join
      • Compilation of Pig code into MapReduce
      • Comparison - Pig vs MapReduce

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    • Module 9: NoSQL Database – HBase

      • NoSQL databases and their need in the industry
      • Introduction to Apache HBase
      • Internals of the HBase architecture
      • The HBase Master and Slave Model
      • Column-oriented
      • 3-dimensional
      • schema-less datastores
      • Data modeling in Hadoop HBase
      • Storing multiple versions of data
      • Data high-availability and reliability
      • Comparison - HBase vs HDFS Comparison - HBase vs RDBMS Data access mechanisms
      • Working with HBase using the shell

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    • Module 10: Data Collection using Sqoop

      • The need for Apache Sqoop
      • Introduction and working of Sqoop
      • Importing data from RDBMS to HDFS
      • Exporting data to RDBMS from HDFS
      • Conversion of data import/export queries into MapReduce jobs

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    • Module 11: Data Collection using Flume

      • What is Apache Flume
      • Flume architecture and aggregation flow
      • Understanding Flume components like data Sources and Sinks
      • Flume channels to buffer events
      • Reliable & scalable data collection tools
      • Aggregating streams using Fan-in
      • Separating streams using Fan-out
      • Internals of the agent architecture
      • Production architecture of Flume
      • Collecting data from different sources to Hadoop HDFS
      • Multi-tier Flume flow for collection of volumes of data using AVRO

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    • Module 12: Apache YARN & advanced concepts in the latest version

      • The need for and the evolution of YARN
      • YARN and its eco-system
      • YARN daemon architecture
      • Master of YARN – Resource Manager
      • Slave of YARN – Node Manager
      • Requesting resources from the application master
      • Dynamic slots(containers). Application execution flow
      • , Hadoop Federation and Namenode HA
      • MapReduce version 2 application over Yarn

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    • Module 13: Processing data with Apache Spark

      • Introduction to Apache Spark
      • Comparison - Hadoop MapReduce vs Apache Spark
      • Spark key features
      • RDD and various RDD operations
      • RDD abstraction
      • interfacing
      • and creation of RDDs Fault Tolerance in Spark
      • The Spark Programming Model
      • Data flow in Spark,The Spark Ecosystem
      • Hadoop compatibility
      • & integration
      • Installation & configuration of Spark
      • Processing Big Data using Spark

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    Price ₹4,999.00 ₹3,599.00
    Instructor Euphoria GenX
    Duration 45 hours
    Enrolled 455 students
    Deadline 4 - 6 Weeks

    Frequently Asked Questions

    1

    Can I learn big data without coding?

    No, coding experience is necessary to get successful as a big data analyst. Hence, if you think of pursuing this career, learn to code.

    2

    Is Python enough for Hadoop?

    Hadoop is a Java-based framework. But it is possible to code Hadoop programs using Python and C++.

    3

    Is Python required for Hadoop?

    For coding in Hadoop, you need expertise in several languages. One of them is Python which is relevant for analysis.

    4

    Which language is best for Hadoop?

    Hadoop is created using Java. So learning this language is a must for those who want a career in this sector.

    5

    Is Hadoop better than SQL?

    Hadoop is a suitable fit when you need to maintain unstructured, semi-structured or structured data. While SQL is a good fit for managing structured and moderate data volume.

    6

    What is the Time Range to learn big data and Hadoop?

    For self- training it might take 3-4 months or more than that. But by joining a professional Big Data Hadoop course, you can learn Hadoop within 1-2 months!

    7

    Is big data and Hadoop easy to learn?

    Learning Big data or Hadoop is not an undoable task! You’ll get an idea after going through different Apache projects and various learning software and videos are also available online. But for the best learning, it’s better to learn it from the experts.

    8

    Is Hadoop good for big data?

    Yes. Hadoop is demandable for the capacity to distribute and parallel processing of huge data amount across several industry-standard servers.

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