Hadoop Online Training course for Data Professionals

Upcoming Batches

Hadoop

30-April-2024

8:00 am

About Course

Why Choose us?
The best teaching staff in the business works with OnlineITvidhya to educate students in the best possible way and instill the subject in them as effectively as is possible. We are the top showcasing pattern for Hadoop course online training. Our support team has years of expertise involving students from the foundational level in learning. They are really persistent, answering your queries as many times as it takes you to understand. Every student will be questioned by our mentors, and they will work with them to steadily develop. Assignments will be given to assess your understanding of the course’s continuity and to enhance your practical knowledge from beginning to end, ensuring that you are progressing in the subject in accordance with the association’s requirements.

Who can learn Hadoop?
People with a basic understanding of PC programming languages or those with an IT background can select the Hadoop course web-based preparation programme, which will add a new skill to their résumé. Hadoop programming is used legally in many professions and can be used as further justification for relocation or for improving your current work package. The outstanding association will cherish this extra skill you learn from us.

What are the prerequisites of OnlineITvidhya’s Hadoop Online Training program?
Depending on the function you want Hadoop to play, you may need to be familiar with a number of computer languages. R or Python, for example, are useful for analysis while Java is better suited for development tasks.

Placement Opportunities
Subject to your skill and correspondence, our teaching staff will assist the students in finding a new line of employment in a reputable organization. We will facilitate practice rounds of fictitious conversations to assist you feel comfortable participating in any group discussions. The Online IT Vidhya Institute staff will go above and beyond to help the students finish the affirmation course by responding to any questions they may have.

Curriculum

Module 1: Hadoop Installation and Setup

The architecture of Hadoop cluster
What is High Availability and Federation?
How to setup a production cluster?
Various shell commands in Hadoop
Understanding configuration files in Hadoop
Installing a single node cluster
Understanding Spark, Scala, Sqoop, Pig, and Flume

Module 2: Introduction to Big Data Hadoop

Introducing Big Data and Hadoop
What is Big Data and where does Hadoop fit in?
Two important Hadoop ecosystem components, namely, MapReduce and HDFS
In-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node, High Availability and in-depth YARN – resource manager and node manager

Module 3: Deep Dive in MapReduce

Learning the working mechanism of MapReduce
Understanding the mapping and reducing stages in MR
Various terminologies in MR like Input Format, Output Format, Partitioners, Combiners, Shuffle, and Sort

Module 4: Introduction to Hive

Introducing Hadoop Hive
Detailed architecture of Hive
Comparing Hive with Pig and RDBMS
Working with Hive Query Language
Creation of a database, table, group by and other clauses
Various types of Hive tables, HCatalog
Storing the Hive Results, Hive partitioning, and Buckets

Module 5: Advanced Hive and Impala

Indexing in Hive
The ap Side Join in Hive
Working with complex data types
The Hive user-defined functions
Introduction to Impala
Comparing Hive with Impala
The detailed architecture of Impala

Module 6: Introduction to Pig

Apache Pig introduction and its various features
Various data types and schema in Hive
The available functions in Pig, Hive Bags, Tuples, and Fields

Module 7: Flume, Sqoop and HBase

Apache Sqoop introduction
Importing and exporting data
Performance improvement with Sqoop
Sqoop limitations
Introduction to Flume and understanding the architecture of Flume
What is HBase and the CAP theorem?

Module 8: Writing Spark Applications Using Scala

Using Scala for writing Apache Spark applications
Detailed study of Scala
The need for Scala
The concept of object-oriented programming
Executing the Scala code
Various classes in Scala like getters, setters, constructors, abstract, extending objects, overriding methods
The Java and Scala interoperability
The concept of functional programming and anonymous functions
Bobsrockets package and comparing the mutable and immutable collections
Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), first Spark application using SBT/Eclipse, Spark Web UI, Spark in Hadoop ecosystem.

Module 9: Use Case Bobsrockets Package

Introduction to Scala packages and imports
The selective imports
The Scala test classes
Introduction to JUnit test class
JUnit interface via JUnit 3 suite for Scala test
Packaging of Scala applications in the directory structure
Examples of Spark Split and Spark Scala

Module 10: Introduction to Spark

Introduction to Spark
Spark overcomes the drawbacks of working on MapReduce
Understanding in-memory MapReduce
Interactive operations on MapReduce
Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
The overview of Spark and how it is better than Hadoop
Deploying Spark without Hadoop
Spark history server

Module 11: Spark Basics

Spark installation guide
Spark configuration
Memory management
Executor memory vs. driver memory
Working with Spark Shell
The concept of resilient distributed datasets (RDD)
Learning to do functional programming in Spark
The architecture of Spark

Module 12: Working with RDDs in Spark

Spark RDD
Creating RDDs
RDD partitioning
Operations and transformation in RDD
Deep dive into Spark RDDs
The RDD general operations
Read-only partitioned collection of records
Using the concept of RDD for faster and efficient data processing
RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

Module 13: Aggregating Data with Pair RDDs

Understanding the concept of key-value pair in RDDs
Learning how Spark makes MapReduce operations faster
Various operations of RDD
MapReduce interactive operations
Fine and coarse-grained update
Spark stack

Module 14: Writing and Deploying Spark Application

Comparing the Spark applications with Spark Shell
Creating a Spark application using Scala or Java
Deploying a Spark application
Scala built application
Creation of the mutable list, set and set operations, list, tuple, and concatenating list
Creating an application using SBT
Deploying an application using Maven
The web user interface of Spark application
A real-world example of Spark
Configuring of Spark

Module 15: Project Solution Discussion

Working towards the solution of the Hadoop project solution
Its problem statements and the possible solution outcomes
Points to focus on scoring the highest marks
Tips for cracking Hadoop interview questions

Module 16: Parallel Processing

Learning about Spark parallel processing
Deploying on a cluster
Introduction to Spark partitions
File-based partitioning of RDDs
Understanding of HDFS and data locality
Mastering the technique of parallel operations
Comparing repartition and coalesce
RDD actions

Module 17: Spark RDD Persistence

The execution flow in Spark
Understanding the RDD persistence overview
Spark execution flow, and Spark terminology
Distribution shared memory vs. RDD
RDD limitations
Spark shell arguments
Distributed persistence
RDD lineage
Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey

Module 18: Spark MLlib

Introduction to Machine Learning
Types of Machine Learning
Introduction to MLlib
Various ML algorithms supported by MLlib
Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

Module 19: Integrating Apache Flume and Apache Kafka

Why Kafka and what is Kafka?
Kafka architecture
Kafka workflow
Configuring Kafka cluster
Operations
Kafka monitoring tools
Integrating Apache Flume and Apache Kafka

Module 20: Spark Streaming

Introduction to Spark Streaming
Features of Spark Streaming
Spark Streaming workflow
Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
Important windowed operators and stateful operators

Module 21: Improving Spark Performance

Introduction to various variables in Spark like shared variables and broadcast variables
Learning about accumulators
The common performance issues
Troubleshooting the performance problems

Module 22: Spark SQL and Data Frames

Learning about Spark SQL
The context of SQL in Spark for providing structured data processing
JSON support in Spark SQL
Working with XML data
Parquet files
Creating Hive context
Writing data frame to Hive
Reading JDBC files
Understanding the data frames in Spark
Creating Data Frames
Manual inferring of schema
Working with CSV files
Reading JDBC tables
Data frame to JDBC
User-defined functions in Spark SQL
Shared variables and accumulators
Learning to query and transform data in data frames
Data frame provides the benefit of both Spark RDD and Spark SQL
Deploying Hive on Spark as the execution engine

Module 23: Scheduling/Partitioning

Learning about the scheduling and partitioning in Spark
Hash partition
Range partition
Scheduling within and around applications
Static partitioning, dynamic sharing, and fair scheduling
Map partition with index, the Zip, and GroupByKey
Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions

Features

Lifetime Access

You will be provided with lifetime access to presentations, quizzes, installation guides and notes.

Assessments

After each training module there will be a quiz to assess your learning.

24*7 Support

We have a lifetime 24*7 Online Expert Support to resolve all your Technical queries.

Forum

We have a community forum for our learners that facilitates further learning through peer interaction and knowledge sharing.

Certificate

After successfully complete your course OnlineITvidhya will give you course completion Certificate.

Mock Interviews

Explore what the real-time interviews expect from you.

Reviews

S

Sharan

Teaching is very good, every scenario he will explain with examples and it is useful for the beginners who want to switch on to the testing platform.

N

Niketan

OnlineITvidhya is an excellent platform to enhance your skills. Thanks to the Trainer and the Team of OnlineITvidhya.

S

Shrinath

I have attended the class daily. It is a super talented trainer. If you really want to learn. This site is awesome for beginners to advanced levels.

FAQs

What if I miss a class?

You will never miss a lecture at OnlineITvidhya You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

What if I have queries after I complete this course?

Your access to the Support Team is for a lifetime and will be available 24/7. The team will help you in resolving queries, during, and after the course.

Can I Attend a Demo Session before Enrolment?

You can get a sample class recording to ensure you are in right place. We ensure you will be getting complete worth of your money by assigning a best instructor in that technology.

What are the diff modes of training that OITV provides?

At OITV, you can enroll in either the instructor-led online training or self-paced training. Apart from this, OnlineITvidhya also offers corporate training for organizations to upskill their workforce. All trainers at OnlineITvidhya have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.

Is it possible to switch from Self paced to instructor led live training?

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

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