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Big Data Course Overview

The Big Data Hadoop certification training is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark. In this hands-on Hadoop course, you will execute real-life, industry-based projects using Integrated Lab. 

Hadoop Development course teaches the skill set required for the learners how to setup Hadoop Cluster, how to store Big Data using Hadoop (HDFS) and how to process/analyze the Big Data using Map-Reduce Programming or by using other Hadoop ecosystems. Attend Hadoop Training demo by Real-Time Expert.

Big Data Hadoop Training Key Features

100% Money Back Guarantee
No questions asked refund*
At Fiesttech, we value the trust of our patrons immensely. But, if you feel that this Big Data Hadoop Training does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 8X higher live interaction in live online classes by industry experts
  • Dedicated mentoring session from our Industry expert faculties
  • Aligned to Cloudera CCA175 certification exam
  • 4 real-life industry projects using Hadoop, Hive and Big data stack
  • Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark

Skills Covered

  • Keras and TensorFlow Framework
  • PyTorch and its elements
  • Image Classification
  • Artificial Neural Networks
  • Autoencoders
  • Deep Neural Networks
  • Conventional Neural Networks
  • Recurrent Neural Networks
  • ADAM Adagrad and Momentum
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Benefits

As humans, we are immersed in data in our every-day lives. As per IBM, the data doubles every two years on this planet. The value that data holds can only be understood when we can start to identify patterns and trends in the data. Normal computing principles do not work when data becomes huge.
There is massive growth in the big data space, and job opportunities are skyrocketing, making this the perfect time to launch your career in this space.
In this course, you will learn Hadoop to drive better business decisions and solve real-world problems.

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+91 844 844 0724

info@fiesttech.com
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Training Options

Explore all of our training options and pick your suitable ones to enroll and start learning with us! We ensure that you will never regret it!

SELF-PACED LEARNING
250
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 5 hands-on projects to perfect the skills learnt
  • 2 simulation test papers for self-assessment
  • 4 Labs to practice live during sessions
  • 24x7 learner assistance and support
INSTRUCTOR-LED TRAINING
350
  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners
  • Interview preparation and Assignments
  • Live Projects detailed for real-world demo

Classes starting from:-

  22ndJan 2024:Weekday Class

  27th Jan 2024:Weekend Class

CORPORATE TRAINING
  • Customized learning delivery model (self-paced and/or instructor-led)
  • Flexible pricing options
  • Enterprise grade learning management system (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Big Data Hadoop Course Curriculum

Eligibility
Big Data Hadoop certification training online course is best suited for IT, Data Management, and Analytics professionals looking to gain expertise in Big Data Hadoop, including Software Developers and Architects, Senior IT professionals, Testing and Mainframe professionals, Business Intelligence professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics.

Pre-requisites
Professionals entering into Big Data Hadoop certification training should have a basic understanding of Core Java and SQL. If you wish to brush up your Core Java skills, FiestTech offers a complimentary self-paced course Java essentials for Hadoop as part of the course syllabus.

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Course Content

Live Course

Self Paced

  • 1.01 - Course Introduction
    4:00
  • 1.02 - 1.2 Accessing Practice Lab
    01:12
  • 2.01 - 1.1 Introduction to Big Data and Hadoop
    02:23
  • 2.02 - 1.2 Introduction to Big Data
    03:23
  • 2.03 - 1.3 Big Data Analytics
    02.11
  • 2.04 - 1.5 Four Vs Of Big Data
    01:26
  • 2.05 - 1.6 Case Study: Royal Bank of Scotland
    05;44
  • 2.06 - 1.7 Challenges of Traditional System
    04:22
  • 2.07 - 1.8 Distributed Systems
    02.11
  • 2.08 - 1.9 Introduction to Hadoop
    01:21
  • 2.09 - 1.10 Components of Hadoop Ecosystem: Part One
    04:22
  • 2.10 - 1.11 Components of Hadoop Ecosystem: Part Two
    02:26
  • 2.11 - 1.12 Components of Hadoop Ecosystem: Part Three
    07:28
  • 2.12 - 1.13 Commercial Hadoop Distributions
    03:31
  • 2.13 - 1.14 Demo: Walkthrough of Fiest Tech Cloudlab
    04:34
  • 2.14 - 1.15 Key Takeaways
    00:32
  • 2.15 - Knowledge Check
    00:57
  • 3.01 - 2.1 Hadoop Architecture Distributed Storage (HDFS) and YARN
    04:32
  • 3.02 - 2.2 What Is HDFS
    03:21
  • 3.03 - 2.3 Need for HDFS
    01:12
  • 3.04 - 2.4 Regular File System vs HDFS
    01:26
  • 3.05 - 2.5 Characteristics of HDFS
    01:26
  • 3.06 - 2.6 HDFS Architecture and Components
    04:34
  • 3.07 - 2.7 High Availability Cluster Implementations
    04:34
  • 3.08 - 2.8 HDFS Component File System Namespace
    03:43
  • 3.09 - 2.9 Data Block Split
    02:26
  • 3.10 - 2.10 Data Replication Topology
    03:23
  • 3.11 - 2.11 HDFS Command Line
    04:22
  • 3.12 - 2.12 Demo: Common HDFS Commands
    08:08
  • 3.13 - 2.13 YARN Introduction
    02:26
  • 3.14 - 2.14 YARN Use Case
    02:11
  • 3.15 - 2.15 YARN and Its Architecture
    07:28
  • 3.16 - 2.16 Resource Manager
    04:32
  • 3.17 - 2.17 How Resource Manager Operates
    02:21
  • 3.18 - 2.18 Application Master
    05:25
  • 3.19 - 2.19 How YARN Runs an Application
    08:43
  • 3.20 - 2.20 Tools for YARN Developers
    09:08
  • 3.21 - 2.21 Demo: Walkthrough of Cluster Part One
    03:11
  • 3.22 - 2.22 Demo: Walkthrough of Cluster Part Two
    09:34
  • 3.23 - 2.23 Key Takeaways
    00:57
  • 3.24 - Knowledge Check
    00:25
  • 3.25 - Hadoop Architecture,Distributed Storage (HDFS) and YARN
    08:08
  • 4.01 - 3.1 Data Ingestion into Big Data Systems and ETL
    02:26
  • 4.02 - 3.2 Data Ingestion Overview Part One
    02:23
  • 4.03 - 3.3 Data Ingestion
    02:26
  • 4.04 - 3.4 Apache Sqoop
    04:22
  • 4.05 - 3.5 Sqoop and Its Uses
    02:32
  • 4.06 - 3.6 Sqoop Processing
    02:11
  • 4.07 - 3.7 Sqoop Import Process
    06:07
  • 4.08 - 3.8 Sqoop Connectors
    04:32
  • 4.09 - 3.9 Demo: Importing and Exporting Data from MySQL to HDFS
    07:28
  • 4.10 - 3.9 Apache Flume
    04:34
  • 4.11 - Apache Sqoop
    03:32
  • 4.12 - 3.10 Flume Model
    03:21
  • 4.13 - 3.11 Scalability in Flume
    05:56
  • 4.14 - 3.12 Components in Flume’s Architecture
    04:32
  • 4.15 - 3.13 Configuring Flume Components
    06:00
  • 4.16 - 3.14 Apache Kafka
    01:26
  • 4.17 - 3.15 Demo: Ingest Twitter Data
    06:07
  • 4.18 - 3.15 Aggregating User Activity Using Kafka
    02:11
  • 4.19 - 3.16 Kafka Data Model
    02:23
  • 4.20 - 3.17 Partitions
    02:27
  • 4.21 - 3.18 Apache Kafka Architecture
    03:21
  • 4.22 - 3.19 Producer Side API Example
    04:34
  • 4.23 - 3.20 Consumer Side API
    05:56
  • 4.24 - 3.21 Demo: Setup Kafka Cluster
    01:21
  • 4.25 - 3.21 Consumer Side API Example
    02:26
  • 4.26 - 3.22 Kafka Connect
    02:32
  • 4.27 - 3.23 Key Takeaways
    00:21
  • 4.28 - 3.26 Demo: Creating Sample Kafka Data Pipeline using Producer an
    08:43
  • 4.29 - Knowledge Check
    00:12
  • 4.30 - Data Ingestion into Big Data Systems and ETL
    08:43
  • 5.01 - 4.1 Distributed Processing MapReduce Framework and Pig
    02.11
  • 5.02 - 4.2 Distributed Processing in MapReduce
    01:26
  • 5.03 - 4.3 Word Count Example
    02:26
  • 5.04 - 4.4 Map Execution Phases
    04:32
  • 5.05 - 4.5 Map Execution Distributed Two Node Environment
    04:32
  • 5.06 - 4.6 MapReduce Jobs
    04:32
  • 5.07 - 4.7 Hadoop MapReduce Job Work Interaction
    02:11
  • 5.08 - 4.8 Setting Up the Environment for MapReduce Development
    04:32
  • 5.09 - 4.9 Set of Classes
    03:11
  • 5.10 - 4.10 Creating a New Project
    05:25
  • 5.11 - 4.11 Advanced MapReduce
    07:28
  • 5.12 - 4.12 Data Types in Hadoop
    02:32
  • 5.13 - 4.13 OutputFormats in MapReduce
    03:23
  • 5.14 - 4.14 Using Distributed Cache
    03:43
  • 5.15 - 4.15 Joins in MapReduce
    01:21
  • 5.16 - 4.16 Replicated Join
    02:21
  • 5.17 - 4.17 Introduction to Pig
    03:11
  • 5.18 - 4.18 Components of Pig
    02.11
  • 5.19 - 4.19 Pig Data Model
    02:32
  • 5.20 - 4.20 Pig Interactive Modes
    04:34
  • 5.21 - 4.21 Pig Operations
    01:26
  • 5.22 - 4.22 Various Relations Performed by Developers
    03:23
  • 5.23 - 4.23 Demo: Analyzing Web Log Data Using MapReduce
    09:08
  • 5.24 - 4.24 Demo: Analyzing Sales Data and Solving KPIs using PIG
    08:43
  • 5.25 - Apache Pig
    02.11
  • 5.26 - 4.25 Demo: Wordcount
    03:21
  • 5.27 - 4.26 Key takeaways
    00:57
  • 5.28 - Knowledge Check
    02:11
  • 5.29 - Distributed Processing - MapReduce Framework and Pig
    03:23
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Big Data Hadoop Training Exam & Certification

Upon successful completion of the Big Data Hadoop certification training, you will be awarded the course completion certificate from FiestTech. To get a CCA175 - Spark and Hadoop certificate from Fiesttech, you need to clear the exam.
 

  • Online Classroom: attend one complete batch of Big Data Hadoop certification training and complete one project and one simulation test with a minimum score of 80%
  • Online Self-learning: complete 85% of the course and complete one project and one simulation test with a minimum score of 80%
FAQS

Big Data Hadoop Training Course FAQs

Big data refers to a collection of extensive data sets, including structured, unstructured, and semi-structured data coming from various data sources and having different formats.These data sets are so complex and broad that they can't be processed using traditional techniques. When you combine big data with analytics, you can use it to solve business problems and make better decisions. 
 

Hadoop is an open-source framework that allows organizations to store and process big data in a parallel and distributed environment. It is used to store and combine data, and it scales up from one server to thousands of machines, each offering low-cost storage and local computation.

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