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Big Data Hadoop Developer Training

Big Data Hadoop Developer Training

About Big Data Hadoop Certification Training

It is a comprehensive Hadoop big data training course designed by industry experts considering current industry job needs to assist you to learn big data Hadoop and Spark modules. this is an industry-recognized big data certification training course that's a combination of the training courses in Hadoop developer, Hadoop administrator, Hadoop testing, and analytics with Apache Spark.

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About Course

What is Big Data Hadoop?

Hadoop is an open-source framework for software programming that took the data storage and processing to next level. With its tremendous capability to store and process large clusters of data, it disclosed opportunities to business around the world with AI. It stores data and runs applications on clusters in commodity hardware that massively reduces the cost of installation and maintenance. It provides vast storage for any type of data, enormous processing power and to have all types of analytics like real-time analytics, predictive analytics data and on at a click of a mouse.

The volume of data handled by organizations keeps growing exponentially with every passing day! This ever-demanding situation requires powerful big data handling solutions like Hadoop for a data-driven decision-making approach.

What does a Hadoop Developer do?

A Hadoop Developer is responsible for programming and development of business applications and software on the Hadoop Platform. They are also involved in designing, developing, installing, configuring, and maintaining the Hadoop application as well as performing the analysis.

Students who begin as Hadoop Developers evolve into Hadoop administrators by the end of a certification course and in the process, guarantee a bright future.

Why learn Big Data and Hadoop?

  • Leading multinational firms are hiring for Hadoop technology – big data & Hadoop market is predicted to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 (Forbes).
  • Streaming Job Opportunities – McKinsey predicts that by 2018 there'll be a shortage of 1.5M data experts (McKinsey Report).
  • Hadoop skills can boost salary packages – Average annual salary of big data Hadoop Developers is around $135k (Indeed.com salary Data).
  • Future of big data and Hadoop looks bright – The world’s technological per-capita capacity to store data has roughly doubled every 40 months since the 1980s; as of 2012, daily 2.5 exabytes (2.5×1018) of data is generated.

What you will learn?

  • Fundamentals of Hadoop and YARN and write applications using them
  • Setting up Pseudo node and Multi-node cluster on Amazon EC2
  • Master HDFS, MapReduce, Hive, Pig, Oozie, Sqoop, Flume, Zookeeper, HBase
  • Learn Spark, Spark SQL, Streaming, DataFrame, RDD, Graphx, MLlib writing Spark applications
  • Master Hadoop administration activities like cluster managing, monitoring, administration and troubleshooting
  • Configuring ETL tools like Pentaho/Talend to work with MapReduce, Hive, Pig, etc
  • Hadoop testing applications using the mr Unit and other automation tools.
  • Work with Avro data formats
  • Practice real-life projects using Hadoop and Apache Spark
  • Be equipped to clear big data Hadoop Certification.

Who should take this course?

Big Data/ Hadoop course is for Students/Non-IT beginners who wish to become an expert in the fastest growing technology.

  • All IT professionals looking forward to becoming a Data scientist in the future
  • Fresher, graduates, or working professionals – whosoever is eager to learn the Big Data technology
  • Hadoop developers looking for learning new verticals like Hadoop Analytics, Hadoop Administration, and Hadoop Testing
  • Mainframe professionals
  • BI/DW/ETL professionals
  • Professionals who want to build effective data processing applications by querying Apache Hadoop.
  • Business Analysts, database administrators, and SQL Developers
  • Software Engineers with a background in ETL/Programming and Managers handling the latest technologies and data management.
  • Technical or Project managers looking for learning of new techniques of managing and maintaining large data and who are involved in the development process can also take active participation in Hadoop Developer classes.
  • .NET Developers and data Analysts who develop applications and perform big data analysis using the Horton works data Platform for Windows will find this useful.
  • Anyone with interest in Big Data analytics

Pre-requisites

  • No Apache Hadoop knowledge is required
  • Fresher from the non-IT background can also excel
  • Prior experience in any programming language might help
  • Basic knowledge of Core Java, UNIX, and SQL
  • Java Essentials for Hadoop course for brushing up one’s skills
  • Good analytical skills to grasp and apply the Hadoop concepts

Big Data/Hadoop Course Highlights

Our online course covers everything from Introduction to big data and Hadoop to advanced topics to assist you to become an expert in Big Data/Hadoop.

  • Excel in Hadoop framework concepts
  • Master in MapReduce framework
  • A detailed clarification and practical examples with special emphasis on HDFS and MapReduce.
  • Scheduling jobs using Oozie
  • Using Sqoop and Flume, learn data loading methods
  • Using Pig, Hive, and Yarn, learn to perform data analytics
  • Learn Hadoop2.x architecture
  • Implementation of advanced usage and indexing
  • Learn Spark and its ecosystem
  • Understanding of Apache Spark and its architecture
  • Introduction to Spark-core, understanding the basic element of Spark -RDD
  • Creating RDDs, Operations in RDD
  • Creating functions in Spark and passing parameters
  • Understanding RDD Transformations and Actions, RDD Persistence and Caching
  • Examples for RDDs
  • Examples of Spark SQL
  • Work on Pig, Apache Hive, Apache HBase, and numerous other big data Hadoop related topics in a simple to understand manner.
  • Learn writing complex MapReduce programs
  • Practice on software tools to gain hands-on expertise.
  • Work on real-time project related situations and examples to provide you the feel of a real work environment.
  • Working on real-life industry-based projects
  • Obtain hands-on experience in Hadoop configuration setup using clusters
  • In-depth understanding of the Hadoop ecosystem
  • Implementation of HBase and MapReduce integration
  • Setting up a Hadoop cluster
  • Implementation of best practices for Hadoop development
  • Group discussions, Mock interview sessions, and Interview questions to prepare you to attend interviews with confidence.
  • Access to the instructor through email to address any questions.
  • Lifetime access to the big data Hadoop online training to assist you to get comfortable with all the concepts and knowledge.

How EnhanceLearn Training can help you

  • 48 Hours of hands-on session per batch and once enrolled you can take any number of batches for 90 days
  • 24x7 Expert Support and GTA (Global Teaching Assistant, SME) support available even to schedule a one on one session for doubt clearing
  • Project Based learning approach with evaluation after each module
  • Project Submission mandatory for Certification and thoroughly evaluated
  • 3 Months Experience Certificate on successful project completion

For becoming a Big Data Expert, choose our best Training and Placement Program. If you are interested in joining the EnhanceLearn team, please email at training@enhancelearn.com.

Course Curriculum

Module 1: Course Introduction

  • Big Data introduction
  • Why is it required?
  • Facts and evolution
  • Objectives
  • Market trends
  • Key features

Module 2: Introduction to Big Data

  • Rise of Big Data
  • Hadoop vs. traditional systems
  • Hadoop master–slave introduction and architecture
  • Objectives
  • Types of data
  • Data explosion
  • Sources
  • Characteristics
  • Knowledge check
  • Traditional IT Analytics approach
  • Capabilities of Big Data technology
  • Discovery and exploration of Big Data technology platform
  • Handling limitations

Module 3: Hadoop Architecture

  • Introduction to Hadoop
  • Architecture
  • History and milestones
  • Objectives
  • Key features
  • Hadoop cluster
  • Core services
  • Role of Hadoop in Big Data
  • Advanced Hadoop core components
  • HDFS introduction
  • Why HDFS
  • Architecture of HDFS
  • VMware player
  • Real-life concept of HDFS
  • Characteristics HDFS
  • File system namespace
  • Data block split
  • Advantages of data block approach
  • Replication method
  • Data replication topology
  • Data replication representation
  • HDFS access
  • Business scenario
  • Error handling
  • Configuration
  • Configuration files
  • Cluster configuration
  • Hadoop modes
  • Terminal commands
  • MapReduce in action
  • Reporting
  • Recovery
  • Overview
  • Important files
  • Parameters and values
  • Environment setup
  • “Include” and “Exclude” configuration files
  • Introduction
  • Objectives
  • Ubuntu server
    • Introduction
    • Installation
    • Business scenario
  • Hadoop installation prerequisites
  • Installation steps
  • Hadoop multi-node installation
    • Single-node cluster
    • Multi-node cluster
  • Clustering of Hadoop environment
  • Introduction
  • Objectives
  • Why MapReduce
  • Characteristics
  • Analogy
  • Examples
  • Map execution
  • Map execution distributed two-node environment
  • Essentials
  • Jobs and associated tasks
  • Business scenario
  • Setup environment
  • Small Data and Big Data
  • Programs
  • Requirements
  • Steps of Hadoop MapReduce
  • Responsibilities of MapReduce
  • Java programming of MapReduce in Eclipse
  • Yarn introduction
  • Objectives of Yarn
  • Real-life concept of Yarn
  • Application master
  • Container
  • Joining data-sets in MapReduce
  • Infrastructure of Yarn
  • Resource manager in Yarn
  • Application running on Yarn
  • Application start-up in Yarn
  • Role of AppMaster in application start-up
  • Hadoop components
  • Advanced HDFS introduction
  • Advanced MapReduce introduction
  • Objectives
  • Business scenario
  • Interfaces
  • Data types in Hadoop
  • Input and output formats in Hadoop
  • Distributed cache
  • Joins in MapReduce
    • Reduce join
    • Composite join
    • Replicated join
  • Errors
  • Overview of the framework
  • Use cases of MapReduce
  • Anatomy of MapReduce framework
  • Mapper class
  • Driver code
  • Understanding partitioner and combiner
  • Hardware considerations
  • Potential problems and solutions
  • Schedulers
  • Balancers
  • Directory structures and files of NameNode/DataNode
  • The checkpoint procedure
  • NameNode failure
  • NameNode recovery
  • Safe mode
  • Adding and removing nodes
  • Metadata and data backup
  • Introduction
    • What is Pig
    • Salient features of Pig
    • Use cases of Pig
    • Interacting with Pig
    • Real-life connect
    • Working of Pig
    • Installing Pig engine
    • Data model
    • Business scenario
    • Relations and commands
  • Basic data analysis
    • Latin syntax
    • Simple data types
    • Loading data
    • Schema
    • Data filtering and sorting
    • Common functions
  • Complex data processing
    • Complex data types
    • Grouping
  • Multi-data-set operations
    • Combining data-sets
    • Methods used for combining
    • Set operations
    • Data-sets split
  • Extended Pig
    • Processing data with Pig using other languages
    • UDFs
    • Macros and import
  • Apache Pig
    • Pig architecture
    • Pig vs. MapReduce
    • Data types
    • Latin relational operators
    • Pig Latin join and CoGroup
    • Pig Latin Group and Union
    • Pig Latin file loaders and UDF
  • Introduction
    • What is Hive
    • Hive schema
    • Hive meta store
    • Data storage
    • Traditional databases
    • Use cases
    • Hive vs. Pig
  • Relational data analysis
    • Databases and tables
    • Data types
    • Joining data-sets
    • Basic syntax
    • Common built-in functions
  • Data management
    • Formats of Hive data
    • Loading data
    • Self-managed tables
    • Databases and tables alteration
  • Optimization
    • Query performance
    • Query optimization
    • Bucketing
    • Partitioning
    • Data indexing
  • Extending Hive
  • User-defined functions
  • Introduction
    • Architecture
    • Objectives
    • Real-life connect with HBase
    • Characteristics
    • Components
    • HBase operations
      • Scan
      • Get
      • Delete
      • Put
      • Business scenario
  • Configuration
  • Fundamentals
  • Installation
  • HBase shell commands
  • What is NOSQL
  • Apache HBase
  • Why HBase
    • Data model
      • Table and row
      • Cell
      • Cell versioning
      • Column qualifier
      • Column family
    • HBase master
    • HBase vs. RDBMS
    • Column families
  • Performance tuning
  • Java-based APIs
  • Introduction
  • CAP theorem
  • Key value stores
    • Riak
    • Memcached
    • Dynamo DB
    • Redis
  • Document store
    • MongoDB
    • CouchDB
  • Graph store
    • Neo4J
  • Column family
    • HBase
    • Cassandra
  • NOSQL vs. SQL
  • NOSQL vs. RDBMS
  • Introduction
  • Big Data ecosystem
  • Data sources
  • Core concepts
  • Anatomy
  • Channel selector
  • Why channels
  • Data ingest
  • Routing and replicating
  • Use cases
    • Log aggregation
  • Adding Flume agent
  • Handling a server farm
  • Data volume per agent
  • Flume deployment example
  • Introduction
  • Uses
  • Benefits
  • Sqoop processing
  • Execution process
  • Import process
  • Connectors
  • Sample commands
  • Events
  • Clients
  • Agents
  • Sinks
  • Source
  • Introduction
  • Ecosystem
  • Real-world view
  • Benefits
  • Updating data in file browser
  • User integration
  • HDFS integration
  • Fundamentals of Hue frontend
  • Introduction
  • Why Oozie
  • Installation
  • Running an example
  • Workflow engine
  • Workflow submission
  • Workflow application
  • Workflow state transitions
  • Coordinator
  • Bundle
  • Time line of Oozie job
  • Abstraction layers
  • Use cases
    • Time triggers
    • Rolling window
    • Data triggers
  • Introduction
  • Data model
  • Service
  • Use cases
  • Znodes
    • Types of Znodes
    • Znodes operations
    • Znodes watches
    • Reads and writes of Znodes
  • Cluster management
  • Leader election
  • Consistency guarantees
  • Objectives
  • Goals and uses
  • SQL
  • Architecture
  • Impala state store
  • Impala catalogue service
  • Query execution phases
  • Introduction
  • Objectives of commercial distribution
  • Cloudera introduction
  • Downloading Cloudera
  • Logging into Hue
  • Cloudera manager
  • Business scenario
  • MapReduce data platform
  • Hortonworks data platform
  • Cloudera CDH
  • Pivotal HD
  • Apache Hadoop ecosystem
  • File system components
  • Serialization components
  • Data store components
  • Job execution components
  • Security components
  • Analytics and intelligence components
  • Data interactions components
  • Data transfer components
  • Search frameworks components
  • Graph-processing framework components
  • Monitoring practices
  • Fair scheduler
  • Configuration of Fair scheduler
  • Schedule of default Hadoop FIFO
  • Troubleshooting and log observation
  • Apache Ambari and its key features
  • Hadoop security
    • Kerberos
    • Authentication mechanism
    • Configuration steps
  • Data confidentiality
  • Usage of trademarks
  • Essentials of Java
  • Objectives
  • JVM – Java Virtual Machine
  • Working of Java
  • Variables in Java
  • Static vs. non-static variables
  • Naming conventions of variables
  • Operators
    • Unary
    • Mathematical
    • Relational
    • Bitwise
    • Logical/conditional
    • Flow control
    • Statements and blocks of code
    • Arrays and strings
    • Classes and methods
    • Access modifiers
  • Java constructors
  • Objectives
  • Salient features
  • Class objects
  • Introduction to packages
  • Naming conventions of packages
  • Introduction to inheritance
  • Types of inheritance
    • Hierarchical
    • Multilevel
  • Method overriding
  • Abstract classes
  • Classes and exceptions in Java
  • Enums of Java
  • Array list
  • Iterators
  • Hashmaps
  • Hashtable class
  • Exceptions
  • Error handling

 

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Roadmap
Big Data Hadoop Developer Training
Certification
big data hadoop developer certificate
Job Overview
Key Features
Training FAQs
What are the advantages of Hadoop training?

Hadoop is changing the perception of handling big data specially unstructured data. It plays a significant role in handling and managing big data by enabling efficient surplus data for any distributed processing system across clusters of computers using easy programming models.

What does a Hadoop Developer do?
Why should I learn Hadoop?
I have knowledge of Linux. Do I get any further benefit while learning Hadoop?
Is SQL knowledge important in Hadoop?
How Hadoop Developer course is different from Hadoop Admin course?
Do you need to know programming for big data hadoop developer?
What platforms and java versions does Hadoop run on?
Can I install Hadoop environment on my MAC machine?
How soon after I enroll would get access to the training course and Content?
Is big data certification and training worth pursuing?
What are the different job roles in Hadoop?
Why do I need to learn Hadoop developers for Big Data?
How is the job market for Hadoop professional currently in US?
How much is the salary of a Hadoop Developer in the United States?
Do you provide demo sessions?
What if I miss a training class or session?
Who are our instructors?
How do you provide training?
What about the Onsite Training Locations?
Is the training interactive, how will it help me to learn?
Will I get to work on a project for this Training?
Do you provide any certification?
How will I get my certificate?
What are the services you provide for job support after training?
Do you provide job placement after the training?
What about the payment process to enroll with the Training?
I am an international student in USA looking for placement?
In how much time will I get a job, if I choose your placement service?
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Reviews
K
Kanika Nayar 
Nice Experience

EnhanceLearn seems to be the best online learning platform for the latest technologies. I highly recommend EnhanceLearn to every learner who is interested in Hadoop training.

J
Jobin Dalal 
Recommeded Training. Better than other training providers.

I am more than satisfied with the Hadoop training provided to me by EnhanceLearn's teachers. I was familiar with the concepts of Big Data Hadoop but EnhanceLearn took it to a different level with their attention to details. Awesome Job EnhanceLearn!

K
Krishna Ram 
Great Course!

The Hadoop training offered by EnhanceLearn.com has delivered more than what was expected. I had a really bad experience in the past with another training company but here on EnhanceLearn, all my pre-purchase questions were clearly answered by the support team. EnhanceLearn provided me with an amazing trainer who helps me boost my knowledge with his Hadoop domain expertise. I would recommend this training to all my friends and everyone who is reading this review.

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