Hadoop Training

❯ Hadoop : What is it?

Hadoop is an open-source software framework for storing Big Data and running applications on clusters of commodity hardware. It is a free, Java-based programming framework. It supports storing and processing large data-sets in a parallel distributed computing environment. It consists of Hadoop common package: a MapReduce engine and the Hadoop distributed file system (HDFS). To start Hadoop, the Hadoop common package has Java Archive (JAR) files and scripts. Other modules include HBase, Hive, Pig, Sqoop, Oozie, Chukwa, Cassandra, Flume, Solr, HCatalog, Spark, Ambari, and Zookeeper which help in faster and easier processing of huge data. It is different conceptually from other relational databases and processes high variety, high volume, and high velocity of data to generate value. With Hadoop, it is possible to run applications on systems with thousands of nodes involving thousands of terabytes of data, which is unfeasible with traditional systems. Hadoop was released in 2008 by Yahoo and now Hadoop’s framework and ecosystem technologies are maintained and managed by the non-profit Apache Software Foundation (ASF) – a global community of software developers and contributors.


 

❯ Key Features of Hadoop Training

  1. One-on-one and weekend classes
  2. Live faculty-led training
  3. Live webinars
  4. 24/7 support from our experts
  5. Experience certificate in Hadoop
  6. Money back guarantee
  7. High-quality e-learning

 

About Hadoop


 

❯ Why Hadoop Training?

We live today in a world of DATA. What-so-ever you do in the Internet, it becomes a source of business information. There has been an exponential growth in the world of data since the last decade. Therefore, industries are looking for ways to handle data and get business. Here is Hadoop – jail-break – for the IT firms in order to store and retrieve large amount of data. Here are some reasons to choose Hadoop.

  1. A combination of online running applications on a huge-scale built of commodity hardware.
  2. Big companies are seeking for Hadoop professionals capable of handling data.
  3. Stores and processes large data-sets in a cost-effective manner.

 

❯ Benefits of Hadoop Training

  1. Highly flexible: No preprocessing of data is required before storing the data. A large or as much amount of data you want can be stored.
  2. Compatible: As Hadoop is a Java-based programming, it works on all platforms.
  3. Scalable: With little administration, one can easily grow their system simply by adding more nodes.
  4. Low cost: This open-source framework is free and allows usage of commodity hardware for storing large quantities of data.
  5. Great computing power: Since Hadoop has a distributed computing environment, with more computing nodes, the processing power increases.
  6. Fault tolerant: In case of hardware failure or if node goes down, jobs automatically redirect to other nodes, making use of distributed computing, and stores multiple copies of the data.
  7. Relevant jobs: After the successful completion of this course, Hadoop architect, Hadoop developer, and Data scientist are the jobs waiting for you next door.

 

❯ Challenges of Using Hadoop

  1. Data security: Fragmented data security issues are still lingering around. Using Kerberos is a great help in making Hadoop environment secure.
  2. MapReduce programming is not a good match for all problems: MapReduce is inefficient for iterative and interactive analytics, as it is file-intensive.
  3. A widely acknowledged talent gap: To be productive in MapReduce, experience holders in Java are required. With a fresher in programming language, fruitful results are difficult to achieve and selecting one such person becomes a BIG NO for employers. Thus, SQL skills are must to easily understand and work with MapReduce techniques.
  4. Full-fledged data management and governance: Hadoop LACKS quality and data standardization tools.

 

❯ Course objectives

  1. Excel in Hadoop framework concepts
  2. Master in MapReduce framework
  3. Scheduling jobs using Oozie
  4. Using Sqoop and Flume, learn data loading methods
  5. Using Pig, Hive, and Yarn, learn performing data analytics
  6. Learn Hadoop2.x architecture
  7. Implementation of advanced usage and indexing
  8. Learn Spark and its ecosystem
  9. Learn working in RDD in Spark
  10. Learn writing complex MapReduce programs
  11. Working on real-life industry-based projects
  12. Obtain hands-on experience in Hadoop configuration setup using clusters
  13. In-depth understanding of Hadoop ecosystem
  14. Implementation of HBase and MapReduce integration
  15. Setting up Hadoop cluster
  16. Implementation of best practices for Hadoop development

 

❯ Who should do this course?

Hadoop has become a cornerstone of every IT sector today. It has now become a must-know technology for the following professionals.

  1. All IT professionals looking forward to become Data scientist in future
  2. Project managers looking for learning of new techniques of managing and maintaining large data
  3. Fresher, graduates, or working professionals – whosoever is eager to learn the Big Data technology
  4. Hadoop developers looking for learning new verticals like Hadoop Analytics, Hadoop Administration, and Hadoop Testing
  5. Mainframe professionals
  6. Software developers and architects
  7. BI/DW/ETL professionals
  8. Anyone with interest in Big Data analytics

 

❯ Pre-requisites

  1. No Apache Hadoop knowledge is required
  2. Fresher from non-IT background can also excel
  3. Prior experience on any programming language might help
  4. Basic knowledge of Core Java, UNIX, and SQL
  5. Java essentials for Hadoop course for brushing up one’s skills
  6. Good analytical skills to grasp and apply the Hadoop concepts

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❯ Course content

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
  • Organizations using Hadoop
  • 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
  • Key features of 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

Module 4 : Hadoop configuration

  • Configuration
  • Configuration files
  • Cluster configuration
  • Hadoop modes
  • Terminal commands
  • MapReduce in action
  • Reporting
  • Recovery

Module 5 : Hadoop cluster configuration

  • Overview
  • Important files
  • Parameters and values
  • Environment setup
  • “Include” and “Exclude” configuration files

Module 6 : Hadoop deployment

  • 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

Module 7 : Introduction to MapReduce

  • 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

Module 8 : Deep dive in MapReduce and Yarn

  • 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 startup in Yarn
  • Role of AppMaster in application startup

Module 9 : Advanced HDFS and MapReduce

  • 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

Module 10 : Understanding the MapReduce framework

  • Overview of the framework
  • Use cases of MapReduce
  • Anatomy of MapReduce framework
  • Mapper class
  • Driver code
  • Understanding partitioner and combiner

Module 11 : Hadoop administration and maintenance

  • 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

Module 12 : Pig

  • 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

Module 13 : Hive

  • Introduction
    • What is Hive
    • Hive schema
    • Hive meta store
    • Data storage
    • Traditional databases
    • Use cases
    • Hive vs. Pig
  • Relational data analysis
    • Databases and tables
      • Managed tables
      • External tables
    • Data types
      • Primitive type
      • Complex type
    • 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

Module 14 : HBase

  • 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

Module 15 : NOSQL

  • 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

Module 16 : Flume

  • 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

 


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Module 17 : Sqoop

  • Introduction
  • Uses
  • Benefits
  • Sqoop processing
  • Execution process
  • Import process
  • Connectors
  • Sample commands
  • Events
  • Clients
  • Agents
  • Sinks
  • Source

Module 18 : Hue

  • Introduction
  • Ecosystem
  • Real-world view
  • Benefits
  • Updating data in file browser
  • User integration
  • HDFS integration
  • Fundamentals of Hue frontend

Module 19 : Oozie

  • 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

Module 20 : ZooKeeper

  • 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

Module 21 : Impala

  • Introduction
  • Objectives
  • Goals and uses
  • SQL
  • Architecture
  • Impala state store
  • Impala catalogue service
  • Query execution phases

Module 22 : Commercial distribution of Hadoop

  • 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

Module 23 : Ecosystem and its components

  • 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

Module 24 : Hadoop monitoring and troubleshooting

  • 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

Module 25 : Java essentials for Hadoop

  • Essentials of Java
    • Brief introduction
    • Objectives
    • What is Java
    • JVM – Java Virtual Machine
    • Working of Java
    • Basic Java program
    • Basic Java syntax
    • Java data types
    • Variables in Java
    • Types of variables
      • Static type
      • Non-static type
    • Static vs. non-static variables
    • Naming conventions of variables
    • Operators
      • Unary
      • Mathematical
      • Relational
      • Bitwise
      • Logical/conditional
    • Flow control
    • Statements and blocks of code
      • If
      • Nested if
      • Loop
      • Switch
      • Break and continue
    • Arrays and strings
    • Classes and methods
    • Access modifiers
  • Java constructors
    • Introduction
    • Objectives
    • Salient features
    • Class objects
    • Constructors
    • Constructor overloading
    • Introduction to packages
    • Naming conventions of packages
    • Introduction to inheritance
    • Types of inheritance
      • Hierarchial
      • Multilevel
    • Method overriding
    • Abstract classes – definition and usage
    • Interfaces – introduction, features, syntax and implementation
    • Input and output
  • Essential classes and exceptions in Java
    • Introduction to classes and exceptions
    • Objectives
    • Enums of Java
    • Array list
      • Introduction
      • Methods
      • Constructors
      • Insertion
    • Iterators
      • Introduction
      • List iterator
      • Displaying items using list iterator
    • Hashmaps
      • Features
      • Methods
      • Constructors
      • Insertion of Hashmap
    • Hashtable class
      • Introduction
      • Methods
      • Constructors
      • Insertion and display
    • Exceptions
      • Exception handling
      • Mechanisms
      • Types
        • Try-catch blocks
        • Multiple catch blocks
      • User-defined exceptions
      • Benefits
      • Error handling

 

Hadoop Developer Salary


 

❯ FAQs

What is Big Data?

The term “Big Data” refers to two things: problem and opportunity. This involves analysis of large data which is not only complicated but unstructured. Companies with big business have used RDBMS and Excel tools for analyzing terabytes of unstructured data-sets. But with technology and time, techniques have evolved and many tools have come up for the purpose like Machine learning, Mahout, SPSS, Teradata, etc. Since last 3–4 years, techniques like Sqoop, Storm, Spark, R, Hadoop, Python, etc. have evolved and became popular to analyze big data.

Big Data is characterized by the high velocity, variety, and volume of data. Big Data triggers the need for a new range of jobs: Data Scientist, Data analysts, R programmers, Python developers, etc. The industries dealing with big data are telecom, ad networks, and financial services.

 


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When are the classes held and what if I miss one?

The classes are conducted both online and offline throughout the week: 1 hour from Monday to Friday and 2 hours each on Saturday and Sunday. In the meantime, you will be given related day-to-day exercises and weekly assignments. At the end of the course completion, you will be allotted a live project to work on and submit on time.

If you miss out to attend a class, you need not to worry, because the session automatically gets recorded which you can access form your account. So you can easily attend the missed class anytime and can re-attend the live lecture in the upcoming Batch. This is advantageous to you because you will not be new to the course and can ask the right questions to get the most out of the course.

 

After I complete the course, what if I have queries?

Relax. Do not panic. You are a lifetime member of the OPT NATION once you have enrolled. You can attend the classes, access the content, or talk to our experts anytime you have a doubt. You just need to submit your query to the support team; our faculty will revert and help you out in any way you want.

 

Why a certificate in Big Data and Hadoop?

In today’s world, the Internet is as much necessary as we eat food. Every industry you step in relies on the Internet. Above all, handling the large amount of data is neither easy manually nor automatically. Here comes the role of Hadoop. Hadoop is capable of storing, retrieving and processing large quantities of data…terabytes of data. Big Data and Hadoop Developers are in huge demand and there are less good ones. The OPT NATION’s certificate in Hadoop will significantly improve your chances to enter the industry you are or have been looking for. By the end of the course, you yourself will be confident and have a good grasp of Hadoop, HDFS, MapReduce, Sqoop, Flume, ZooKeeper, Hive, HBase, Pig, etc.

 

Is Java the necessary prerequisite to undertake Hadoop course?

You can master in Hadoop, even if you are not from IT background. But any programming language, such as Java, C#, PHP, Python, C, C++, .NET, PERL, etc., if you know could be a great help. Even if you have no knowledge regarding Java, do not worry, because we have “Java Essentials for Hadoop” course to brush up your skills and that too absolutely free.

 

For how long is my OPT NATION login and password valid to access my course?

There is no end date or validity once you have enrolled for the course. There is lifetime validity even after the completion of the course for accessing the content from anywhere anytime.

 

Is online learning effective to become an expert on Hadoop?

Yes, it is. Various benefits online training provides are:

  1. Attend the class anytime and from anywhere
  2. Course material accessible life time
  3. Immediate doubts clarification
  4. Experts of the field are your faculty
  5. Saves time and expense

 

How do I get my certificate?

After successful completion of the course and related assignments, exercises and exams, your certificate will be emailed to you.

 

What if I fail to clear my certification course in first attempt?

You can anytime ask for assistance to your queries and doubts and reappear for the certification exam. Once you have enrolled, you are a lifetime member and so you will be charged nothing for reappearing. Our experts are here to help you out in all possible ways.

 

Do you provide placements?

Yes, we do. OPT NATION is one of those rare online training centers that apart from teaching you provide placements at your home. You just need to concentrate to excel in your course; in return, OPT NATION will give you a platform to exhibit the plethora of your skills.

 


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