Big Data Hadoop and Spark Developer Certification Training

Big Data Hadoop and Spark Developer Certification Training

Edureka’s Big Data Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this online instructor-led Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka’s Cloud Lab.

Understanding Big Data and Hadoop

Learning Objectives: In this module, you will understand what Big Data is, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.

Topics:

  • Introduction to Big Data & Big Data Challenges Preview
  • Limitations & Solutions of Big Data Architecture
  • Hadoop & its Features
  • Hadoop Ecosystem
  • Hadoop 2.x Core Components Preview
  • Hadoop Storage: HDFS (Hadoop Distributed File System)
  • Hadoop Processing: MapReduce Framework
  • Different Hadoop Distributions

 

Hadoop Architecture and HDFS

Learning Objectives: In this module, you will learn Hadoop Cluster Architecture, important configuration files of Hadoop Cluster, Data Loading Techniques using Sqoop & Flume, and how to setup Single Node and Multi-Node Hadoop Cluster.

Topics:

  • Hadoop 2.x Cluster Architecture Preview
  • Federation and High Availability Architecture Preview
  • Typical Production Hadoop Cluster
  • Hadoop Cluster Modes
  • Common Hadoop Shell Commands Preview
  • Hadoop 2.x Configuration Files
  • Single Node Cluster & Multi-Node Cluster set up
  • Basic Hadoop Administration

 

Hadoop MapReduce Framework

Learning Objectives: In this module, you will understand Hadoop MapReduce framework comprehensively, the working of MapReduce on data stored in HDFS. You will also learn the advanced MapReduce concepts like Input Splits, Combiner & Partitioner.

Topics:

  • Traditional way vs MapReduce way
  • Why MapReduce Preview
  • YARN Components
  • YARN Architecture
  • YARN MapReduce Application Execution Flow
  • YARN Workflow
  • Anatomy of MapReduce Program Preview
  • Input Splits, Relation between Input Splits and HDFS Blocks
  • MapReduce: Combiner & Partitioner
  • Demo of Health Care Dataset
  • Demo of Weather Dataset

 

Advanced Hadoop MapReduce

Learning Objectives: In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.

Topics:

  • Counters
  • Distributed Cache
  • MRunit
  • Reduce Join Preview
  • Custom Input Format Preview
  • Sequence Input Format
  • XML file Parsing using MapReduce

 

Apache Pig

Learning Objectives: In this module, you will learn Apache Pig, types of use cases where we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, Pig running modes, Pig UDF, Pig Streaming & Testing Pig Scripts. You will also be working on healthcare dataset.

 

Topics:

  • Introduction to Apache Pig Preview
  • MapReduce vs Pig
  • Pig Components & Pig Execution
  • Pig Data Types & Data Models in Pig
  • Pig Latin Programs Preview
  • Shell and Utility Commands
  • Pig UDF & Pig Streaming
  • Testing Pig scripts with Punit
  • Aviation use-case in PIG
  • Pig Demo of Healthcare Dataset

 

Apache Hive

Learning Objectives: This module will help you in understanding Hive concepts, Hive Data types, loading and querying data in Hive, running hive scripts and Hive UDF.

Topics:

  • Introduction to Apache Hive Preview
  • Hive vs Pig
  • Hive Architecture and Components Preview
  • Hive Metastore
  • Limitations of Hive
  • Comparison with Traditional Database
  • Hive Data Types and Data Models
  • Hive Partition
  • Hive Bucketing
  • Hive Tables (Managed Tables and External Tables)
  • Importing Data
  • Querying Data & Managing Outputs
  • Hive Script & Hive UDF
  • Retail use case in Hive
  • Hive Demo on Healthcare Dataset

 

Advanced Apache Hive and HBase

Learning Objectives: In this module, you will understand advanced Apache Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, and optimizations in Hive. You will also acquire indepth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components.

Topics:

  • Hive QL: Joining Tables, Dynamic Partitioning Preview
  • Custom MapReduce Scripts
  • Hive Indexes and views Preview
  • Hive Query Optimizers
  • Hive Thrift Server
  • Hive UDF Preview
  • Apache HBase: Introduction to NoSQL Databases and HBase Preview
  • HBase v/s RDBMS
  • HBase Components
  • HBase Architecture Preview
  • HBase Run Modes
  • HBase Configuration
  • HBase Cluster Deployment

 

Advanced Apache HBase

Learning Objectives: This module will cover advance Apache HBase concepts. We will see demos on HBase Bulk Loading & HBase Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.

Topics:

  • HBase Data Model Preview
  • HBase Shell
  • HBase Client API
  • Hive Data Loading Techniques
  • Apache Zookeeper Introduction
  • ZooKeeper Data Model
  • Zookeeper Service
  • HBase Bulk Loading Preview
  • Getting and Inserting Data
  • HBase Filters

 

Processing Distributed Data with Apache Spark

Learning Objectives: In this module, you will learn what is Apache Spark, SparkContext & Spark Ecosystem. You will learn how to work in Resilient Distributed Datasets (RDD) in Apache Spark. You will be running application on Spark Cluster & comparing the performance of MapReduce and Spark.

Topics:

  • What is Spark Preview
  • Spark Ecosystem
  • Spark Components Preview
  • What is Scala Preview
  • Why Scala
  • SparkContext
  • Spark RDD

 

Oozie and Hadoop Project

Learning Objectives: In this module, you will understand how multiple Hadoop ecosystem www.edureka.co © 2019 Brain4ce Education Solutions Pvt. Ltd. All rights Reserved. components work together to solve Big Data problems. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.

Topics:

  • Oozie Preview
  • Oozie Components
  • Oozie Workflow
  • Scheduling Jobs with Oozie Scheduler
  • Demo of Oozie Workflow
  • Oozie Coordinator Preview
  • Oozie Commands
  • Oozie Web Console
  • Oozie for MapReduce
  • Combining flow of MapReduce Jobs
  • Hive in Oozie
  • Hadoop Project Demo
  • Hadoop Talend Integration

 

Certification Project

Analyses of an Online Book Store

  • Find out the frequency of books published each year. (Hint: Sample dataset will be provided)
  • Find out in which year the maximum number of books were published
  • Find out how many books were published based on ranking in the year 2002.

Sample Dataset Description

  • The Book-Crossing dataset consists of 3 tables that will be provided to you.

Airlines Analysis

  • Find list of Airports operating in Country India
  • Find the list of Airlines having zero stops
  • List of Airlines operating with codeshare
  • Which country (or) territory having highest Airports
  • Find the list of Active Airlines in United state

Sample Dataset Description

In this use case, there are 3 data sets. Final_airlines, routes.dat, airports_mod.dat

Edureka’s Big Data Hadoop Certification training is meant to help you learn and master the entire hadoop ecosystem. With our industry relevant course catalog, we make sure that the learning is in line with how the technology is being used in the market today. We also have real-time projects for our learners to work on for better hands-on. With our cloud lab implementation, we provide the perfect environment for all learners to gain as much practical experience possible.
First step is always the most important and the hardest one to take. We understand that before you are serious enough about getting certified, you need to know more about the technology. Our Youtube channel and blogs have a lot of tutorials on the Hadoop ecosystem. These tutorials is all you need to get your basics cleared and get started with Hadoop.
Edureka’s Hadoop certification training will help you master the concepts and practical implementation of the technology in 1 months time. With dedicated resources and a never-back-down attitude, you can master the technology in one month.
Learning pedagogy has evolved a lot with the advent of technology. These changes and advancements have made it possible to increase your efficiency while you learn. While the traditional classroom based training has proven to be successful, with online learning learners have flexibility in terms of schedule. Apart from this, they can visit the study material anytime from anywhere and brush up on concepts with ease. Learning does not stop once the classes are over, which is why we also provide a 24x7 support system to help you with your doubts even after your class ends.
Hadoop developers are in great demand in the IT sector of the US. Depending on the experience and the expertise you bring to the table, the average salary can range from $120,000/- to $180,000/-
There are no such prerequisites for Big Data & Hadoop Course. However, prior knowledge of Core Java and SQL will be helpful but is not mandatory. Further, to brush up your skills, Edureka offers a complimentary self-paced course on "Java essentials for Hadoop" when you enroll for the Big Data and Hadoop Course.
Edureka’s Big Data Hadoop Certification training is meant to help you learn and master the entire hadoop ecosystem. With our industry relevant course catalog, we make sure that the learning is in line with how the technology is being used in the market today. We also have real-time projects for our learners to work on for better hands-on. With our cloud lab implementation, we provide the perfect environment for all learners to gain as much practical experience possible.
Diamonds are forever, and so is our support to you. The more queries you come up with, more happy we are, as it is a strong indication of your effort to learn. Our Instructors will answer all your queries during classes, PLMs will be available to resolve any functional or technical query and we will even go to lengths of solving your doubts via screen sharing. If you are committed to learn, we are Ridiculously Committed to make you learn.
Our instructors are expert professionals with more than 10 years of experience, selected after a stringent process. Besides technology expertise, we look for passion and joy for teaching in our Instructors. After shortlisting, they undergo a 3 months long training program. All instructors are reviewed by learners for every session they take, and they have to keep a consistent rating above 4.5+ to be a part of Edureka Faculty.
If you have seen any of our sample class recordings, you don't need to look further. Enrollment is a commitment between you and us where you promise to be a good learner and we promise to provide you the best ecosystem possible for learning. Our sessions are a significant part of your learning, standing on the pillars of learned and helpful instructors, dedicated Personal Learning Managers and interactions with your peers. So experience complete learning instead of a demo session. In any case, you are covered by Edureka Guarantee, our No questions asked, 100% refund policy.
More than 70% of Edureka Learners have reported change in job profile (promotion), work location (onsite), lateral transfers & new job offers. Edureka's certification is well recognized in the IT industry as it is a testament to the intensive and practical learning you have gone through and the real life projects you have delivered.
Do you know attendance rate in all Edureka Live sessions is 83%? You will never miss a class at Edureka. Your learning will be monitored by Edureka's Personal Learning Manager (PLM) and our Assured Learning Framework, which will ensure you attend all classes and get the learning and certification you deserve. In case you are not able to attend any lecture, you can view the recorded session of the class in Edureka's Learning Management System(LMS). To make things better for you, we also provide the facility to attend the missed session in any other live batch. Now you see why we say we are "Ridiculously Committed!"

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Enrolled: 650 students
Duration: 30
Video: 30
Level: Intermediate

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