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Big Data Hadoop Developer

Course number: CGIBDHD40

Hadoop is an Apache project (i.e. an open source software) to store and process Big Data. Hadoop stores Big Data in a distributed  and fault-tolerant manner over commodity hardware. Afterwards, Hadoop tools are used to perform parallel data processing over HDFS (Hadoop Distributed File System).

As organizations have realized the benefits of Big Data Analytics, so there is a huge demand for Big Data & Hadoop professionals. Companies are looking for Big Data & Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume.

Hadoop Training is designed to help you become a certified Big Data practitioner by providing you rich hands-on training on Hadoop Ecosystem. This Hadoop developer certification training is a stepping stone to your Big Data journey, and you will get the opportunity to work on various Big data projects.

Mastering Hadoop and related tools: The course provides you with an in-depth understanding of the Hadoop framework including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion.

Mastering real-time data processing using Spark: You will learn to do functional programming in Spark, implement Spark applications, understand parallel processing in Spark, and use Spark RDD optimization techniques. You will also learn the various interactive algorithm in Spark and use Spark SQL for creating, transforming, and querying data form.

As a part of the course, you will be required to execute real-life industry-based projects using CloudLab. The projects included are in the domains of Banking, Telecommunication, Social media, Insurance, and E- commerce. This Big Data course also prepares you for the Cloudera CCA175 certification.

Objectives

Big Data Hadoop Certification Training is designed by industry experts to help you become a  Certified Big Data Practitioner. The Big Data Hadoop course offers:

  • In-depth knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) & MapReduce
  • Comprehensive knowledge of various tools that fall in Hadoop Ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase
  • The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
  • The exposure to many real world industry-based projects which will be executed in Edureka’s CloudLab
  • Projects which are diverse in nature covering various data sets from multiple domains such as banking, telecommunication, social media, insurance, and e-commerce
  • Rigorous involvement of a Hadoop expert throughout the Big Data Hadoop Training to learn industry standards and best practices
Prerequisites
  • There are no prerequisites for this course. However, prior knowledge of Core Java and SQL will be helpful, but is not mandatory.
Target Audience

The market for Big Data analytics is growing across the world, and this strong growth pattern translates into a great opportunity for IT professionals. Hiring managers are looking for certified Big Data Hadoop professionals. Our Big Data & Hadoop Certification Training helps you to take advantage of this opportunity and accelerate your career. Our Big Data Hadoop Course can be pursued by current professionals as well as those who are new to the industry.  It is best suited for:

  • Software Developers
  • Project Managers
  • Software Architects
  • ETL and Data Warehousing Professionals
  • Data Engineers
  • Data Analysts & Business Intelligence Professionals
  • DBAs and DB professionals
  • Senior IT Professionals
  • Testing professionals
  • Mainframe professionals
  • Graduates looking to build a career in the Big Data Field
Certification

Big Data Hadoop Developer by Cloudera

Exam

Cloudera CCA175 - Big Data

Accreditation

Post class completion, students can appear for the Cloudera CCA175 - Big Data exam.
Students will also receive a “Certificate of Completion” from COMNet Group Inc.

Course Outline
Lesson 1: Understanding Big Data and Hadoop

In this lesson, you will learn 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 Limitations & Solutions of Big Data Architecture
  • Hadoop & its Features
  • Hadoop Ecosystem
  • Hadoop 2.x Core Components
  • Hadoop Storage: HDFS (Hadoop Distributed File System)
  • Hadoop Processing: MapReduce Framework
  • Different Hadoop Distributions
Lesson 2: Hadoop Architecture and HDFS

In this lesson, 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
  • Federation and High Availability Architecture
  • Hadoop Cluster Modes
  • Common Hadoop Shell Commands
  • Hadoop 2.x Configuration Files
  • Single Node Cluster & Multi-Node Cluster set up
  • Basic Hadoop Administration
Lesson 3: Hadoop MapReduce Framework

In this lesson, 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
  • YARN Components
  • YARN Architecture
  • YARN MapReduce Application Execution Flow
  • YARN Workflow
  • Anatomy of MapReduce Program
  • Input Splits, Relation between Input Splits and HDFS Blocks
  • MapReduce: Combiner & Partitioner
  • Demo of Health Care Dataset
  • Demo of Weather Dataset
Lesson 4: Advanced Hadoop Map Reduce

In this lesson, 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
  • Custom Input Format
  • Sequence Input Format
  • XML file Parsing using MapReduce
Lesson 5: Apache Pig

In this lesson, 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
  • MapReduce vs Pig
  • Pig Components & Pig Execution
  • Pig Data Types & Data Models in Pig
  • Pig Latin Programs
  • Shell and Utility Commands
  • Pig UDF & Pig Streaming
  • Aviation use-case in PIG
  • Pig Demo of Healthcare Dataset
Lesson 6: Apache Hive

This lesson 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
  • Hive vs Pig
  • Hive Architecture and Components
  • 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
Lesson 7: Advanced Apache Hive and HBase

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

Topics:

  • Hive QL: Joining Tables, Dynamic Partitioning
  • Custom MapReduce Scripts
  • Hive Indexes and views
  • Hive Query Optimizers
  • Hive Thrift Server
  • Hive UDF
  • Apache HBase: Introduction to NoSQL Databases and HBase
  • HBase v/s RDBMS
  • HBase Components
  • HBase Architecture
  • HBase Run Modes
  • HBase Configuration
  • HBase Cluster Deployment
Lesson 8: Advanced Apache HBase

This lesson 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, and why HBase uses Zookeeper.

Topics:

  • HBase Data Model
  • HBase Client API
  • Hive Data Loading Techniques
  • Apache Zookeeper Introduction
  • ZooKeeper Data Model
  • Zookeeper Service
  • HBase Bulk Loading
  • Getting and Inserting Data
  • HBase Filters
Lesson 9: Processing Distributed Data with Apache Spark

In this lesson, you will learn about Apache Spark, SparkContext & Spark Ecosystem. You will learn how to work in Resilient Distributed Datasets (RDD) in Apache Spark. You will be running applications on Spark Cluster & comparing the performance of MapReduce and Spark.

Topics :

  • What is Spark
  • Spark Ecosystem
  • Spark Components
  • What is Scala
  • Why Scala
  • SparkContext
  • Spark RDD
Lesson 10: Oozie and Hadoop Project

In this lesson, you will learn how multiple Hadoop ecosystem 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
  • Oozie Components
  • Oozie Workflow
  • Scheduling Jobs with Oozie Scheduler
  • Demo of Oozie Workflow
  • Oozie Coordinator
  • Oozie Commands
  • Oozie Web Console
  • Oozie for MapReduce
  • Combining flow of MapReduce Jobs
  • Hive in Oozie
  • Hadoop Project Demo
  • Hadoop Talend Integration
Lesson 11: Certification Project

1) Analyses of an Online Book Store

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

Sample Dataset Description

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

Airlines Analysis

  1. Find the list of Airports operating in the country of India
  2. Find the list of Airlines having zero stops
  3. List of Airlines operating with code share
  4. Which country (or) territory has the highest number of Airports
  5. Find the list of Active Airlines in the United States

Sample Dataset Description

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

Available Formats

Classroom
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