Master in Big Data Hadoop
Course

(5.0) G 4.5/5 f 4.5/5
Course Duration

72 hours

Live Project

02

Certification

Industry recognized

Training Format

Online/Classroom/Corporate

images

8000+

Certified Learners

15+

Years Avg. faculty experience

40+

Happy Clients

4.5/5.0

Average class rating

ABOUT MASTER IN BIG DATA HADOOP COURSE


Big Data Hadoop training program helps you master the concepts of Big Data Hadoop and Spark framework to get ready for the Cloudera CCA Spark and master Hadoop Administration with real-time industry-oriented case-study projects. In this Big Data course, you Learn how various components of the Hadoop ecosystem fit into the Big Data processing lifecycle.

MASTER IN BIG DATA HADOOP COURSE OVERVIEW


Our Big Data Hadoop training is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark.It is a comprehensive Hadoop Big Data training course designed by industry experts considering current industry job requirements to help you learn Big Data Hadoop and Spark modules. In this hands-on Hadoop course, you will execute real-life, industry-based projects using Integrated Lab.This is an industry-recognized Big Data Hadoop certification training course that is a combination of the training courses in Hadoop developer, Hadoop administrator, Hadoop testing and analytics with Apache Spark.

Instructor-led, Live & Interactive Sessions


Duration
Mode
Agenda
72 Hours
Online (Instructor-led)
Big Data Hadoop Training Certification

Course Price at

49,999/-



[Fixed - No Negotiations]



How we prepare you


Big Data Hadoop

Upon completion of this program you will get 360-degree understanding of Big Data Hadoop. This course will give you thorough learning experience in terms of understanding the concepts, mastering them thoroughly and applying them in real work environment.

Hands-on experience in a live project

You will be given industry level real time projects to work on and it will help you to differentiate yourself with multi-platform fluency, and have real-world experience with the most important tools and platforms.


Unlimited Mock Interview and Quiz Session

As part of this, You would be given complete interview preparations kit, set to be ready for the Big Data Hadoop. This kit has been crafted by 200+ years industry experience and the experiences of nearly 10000 DevOpsSchool's Machine Learning learners worldwide.

Agenda of the Master in Big Data Hadoop Training CourseDownload Curriculum


  • Installation and Setup Hadoop
  • Introduction to Big Data Hadoop and Understanding HDFS and MapReduce
  • Deep Dive in MapReduce
  • Introduction to Hive
  • Advanced Hive and Impala
  • Introduction to Pig
  • Flume, Sqoop and HBase
  • Writing Spark Applications Using Scala
  • Spark framework
  • RDD in Spark
  • Data Frames and Spark SQL
  • Machine Learning Using Spark (MLlib)
  • Integrating Apache Flume and Apache Kafka
  • Spark Streaming
  • Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2

  • 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 with Cloudera Manager
  • Understanding Spark, Scala, Sqoop, Pig, and Flume
  • 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
Hands-on Exercises:
  • 1. HDFS working mechanism
  • 2. Data replication process
  • 3. How to determine the size of the block?
  • 4. Understanding a data node and name node
  • 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
Hands-on Exercises:
  • 1. How to write a WordCount program in MapReduce?
  • 2. How to write a Custom Partitioner?
  • 3. What is a MapReduce Combiner?
  • 4. How to run a job in a local job runner
  • 5. Deploying a unit test
  • 6. What is a map side join and reduce side join?
  • 7. What is a tool runner?
  • 8. How to use counters, dataset joining with map side, and reduce side joins?
  • 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
Hands-on Exercises:
  • 1. Database creation in Hive
  • 2. Dropping a database
  • 3. Hive table creation
  • 4. How to change the database?
  • 5. Data loading
  • 6. Dropping and altering table
  • 7. Pulling data by writing Hive queries with filter conditions
  • 8. Table partitioning in Hive
  • 9. What is a group by clause?
  • 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
Hands-on Exercises:
  • 1. How to work with Hive queries?
  • 2. The process of joining the table and writing indexes
  • 3. External table and sequence table deployment
  • 4. Data storage in a different table
  • Apache Pig introduction and its various features
  • Various data types and schema in Hive
  • The available functions in Pig, Hive Bags, Tuples, and Fields
Hands-on Exercises:
  • 1. Working with Pig in MapReduce and local mode
  • 2. Loading of data
  • 3. Limiting data to 4 rows
  • 4. Storing the data into files and working with Group By, Filter By, Distinct, Cross, Split in Hive
  • 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?
Hands-on Exercises:
  • 1. Working with Flume to generate Sequence Number and consume it
  • 2. Using the Flume Agent to consume the Twitter data
  • 3. Using AVRO to create Hive Table
  • 4. AVRO with Pig
  • 5. Creating Table in HBase
  • 6. Deploying Disable, Scan, and Enable Table
  • 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.
Hands-on Exercises:
  • 1. Writing Spark application using Scala
  • 2. Understanding the robustness of Scala for Spark real-time analytics operation
  • Detailed Apache Spark and its various features
  • Comparing with Hadoop
  • Various Spark components
  • Combining HDFS with Spark and Scalding
  • Introduction to Scala
  • Importance of Scala and RDD
Hands-on Exercises:
  • 1. The Resilient Distributed Dataset (RDD) in Spark
  • 2. How does it help to speed up Big Data processing?
  • Understanding the Spark RDD operations
  • Comparison of Spark with MapReduce
  • What is a Spark transformation?
  • Loading data in Spark
  • Types of RDD operations viz. transformation and action
  • What is a Key/Value pair?
Hands-on Exercises:
  • 1. How to deploy RDD with HDFS?
  • 2. Using the in-memory dataset
  • 3. Using file for RDD
  • 4. How to define the base RDD from an external file?
  • 5. Deploying RDD via transformation
  • 6. Using the Map and Reduce functions
  • 7. Working on word count and count log severity
  • The detailed Spark SQL
  • The significance of SQL in Spark for working with structured data processing
  • Spark SQL JSON support
  • Working with XML data and parquet files
  • Creating Hive Context
  • Writing Dataframe to Hive
  • How to read a JDBC file?
  • Significance of a Spark data frame
  • How to create a data frame?
  • What is schema manual inferring?
  • Work with CSV files, JDBC table reading, data conversion from Data Frame to JDBC, Spark SQL user-defined functions, shared variable, and accumulators
  • How to query and transform data in Data Frames?
  • How data frames provide the benefits of both Spark RDD and Spark SQL?
  • Deploying Hive on Spark as the execution engine
Hands-on Exercises:
  • 1. Data querying and transformation using Data Frames
  • 2. Finding out the benefits of Data Frames over Spark SQL and Spark RDD
  • Introduction to Spark MLlib
  • Understanding various algorithms
  • What is Spark iterative algorithm?
  • Spark graph processing analysis
  • Introducing Machine Learning
  • K-Means clustering
  • Spark variables like shared and broadcast variables
  • What are accumulators?
  • Various ML algorithms supported by MLlib
  • Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques
Hands-on Exercises:
  • 1. Building a recommendation engine
  • Why Kafka?
  • What is Kafka?
  • Kafka architecture
  • Kafka workflow
  • Configuring Kafka cluster
  • Basic operations
  • Kafka monitoring tools
  • Integrating Apache Flume and Apache Kafka
Hands-on Exercises:
  • 1. Configuring Single Node Single Broker Cluster
  • 2. Configuring Single Node Multi Broker Cluster
  • 3. Producing and consuming messages
  • 4. Integrating Apache Flume and Apache Kafka.
  • Introduction to Spark streaming
  • The architecture of Spark streaming
  • Working with the Spark streaming program
  • Processing data using Spark streaming
  • Requesting count and DStream
  • Multi-batch and sliding window operations
  • Working with advanced data sources
  • 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 its uses
  • Important Windowed operators and Stateful operators
Hands-on Exercises:
  • 1. Twitter Sentiment analysis
  • 2. Streaming using Netcat server
  • 3. Kafka-Spark streaming
  • 4. Spark-Flume streaming
  • Create a 4-node Hadoop cluster setup
  • Running the MapReduce Jobs on the Hadoop cluster
  • Successfully running the MapReduce code
  • Working with the Cloudera Manager setup
Hands-on Exercises:
  • 1. The method to build a multi-node Hadoop cluster using an Amazon EC2 instance
  • 2. Working with the Cloudera Manager
  • Overview of Hadoop configuration
  • The importance of Hadoop configuration file
  • The various parameters and values of configuration
  • The HDFS parameters and MapReduce parameters
  • Setting up the Hadoop environment
  • The Include and Exclude configuration files
  • The administration and maintenance of name node, data node directory structures, and files
  • What is a File system image?
  • Understanding Edit log
Hands-on Exercises:
  • 1. The process of performance tuning in MapReduce
  • Introduction to the checkpoint procedure, name node failure
  • How to ensure the recovery procedure, Safe Mode, Metadata and Data backup, various potential problems and solutions, what to look for and how to add and remove nodes
Hands-on Exercises:
  • 1. How to go about ensuring the MapReduce File System Recovery for different scenarios
  • 2. JMX monitoring of the Hadoop cluster
  • 3. How to use the logs and stack traces for monitoring and troubleshooting
  • 4. Using the Job Scheduler for scheduling jobs in the same cluster
  • 5. Getting the MapReduce job submission flow
  • 6. FIFO schedule
  • 7. Getting to know the Fair Scheduler and its configuration
  • How ETL tools work in the Big Data industry?
  • Introduction to ETL and data warehousing
  • Working with prominent use cases of Big Data in ETL industry
  • End-to-end ETL PoC showing Big Data integration with ETL tool
Hands-on Exercises:
  • 1. Connecting to HDFS from ETL tool
  • 2. Moving data from Local system to HDFS
  • 3. Moving data from DBMS to HDFS,
  • 4. Working with Hive with ETL Tool
  • 5. Creating MapReduce job in ETL tool
  • Working towards the solution of the Hadoop project solution
  • Its problem statements and the possible solution outcomes
  • Preparing for the Cloudera certifications
  • Points to focus on scoring the highest marks
  • Tips for cracking Hadoop interview questions
Hands-on Exercises:
  • 1. The project of a real-world high value Big Data Hadoop application
  • 2. Getting the right solution based on the criteria set by the Intellipaat team

Importance of testing

Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end-to-end tests, Functional testing, Release certification testing, Security testing, Scalability testing, Commissioning and Decommissioning of data nodes testing, Reliability testing, and Release testing

  • Understanding the Requirement
  • Preparation of the Testing Estimation

Test Cases, Test Data, Test Bed Creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, Hive and HBase) while loading the input (logs, files, records, etc.) using Sqoop/Flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges, etc.), reporting defects to the development team or manager and driving them to closure

  • Consolidating all the defects and create defect reports
  • Validating new feature and issues in Core Hadoop
  • Report defects to the development team or manager and driving them to closure
  • Consolidate all the defects and create defect reports
  • Responsible for creating a testing framework called MRUnit for testing of MapReduce programs
  • Automation testing using the OOZIE
  • Data validation using the query surge tool
  • Test plan for HDFS upgrade
  • Test automation and result
  • Test, install and configure
PROJECT

In Big Data Hadoop Course a Participant will get total 5 real time scenario based projects to work on, as part of these projects, we would help our participant to have first hand experience of real time scenario based software project development planning, coding, deployment, setup and monitoring in production from scratch to end. We would also help our participants to visualize a real development environment, testing environment and production environments.

Interview

As part of this, You would be given complete interview preparations kit, set to be ready for the Big Data Hadoop hotseat. This kit has been crafted by 200+ years industry experience and the experiences of nearly 10000 DevOpsSchool Machine Learning learners worldwide.

OUR COURSE IN COMPARISON

FEATURES DEVOPSSCHOOL OTHERS
1 Course for Big Data Hadoop
Faculty Profile Check
Lifetime Technical Support
Lifetime LMS access
Top 46 Tools
Mock Interviews after Training
Training Notes
Step by Step Web Based Tutorials
Training Slides
Training + Additional Videos

Upskilling in the Big Data and Analytics field is a smart career decision. According to the Market Research, the global Hadoop market will reach $84.6 Billion by 2021 and there is a shortage of 1.4-1.9 million Hadoop data analysts in the U.S. alone.Big Data is the fastest growing and the most promising technology for handling large volumes of data for doing data analytics. This Big Data Hadoop training will help you be up and running in the most demanding professional skills. Almost all top MNCs are trying to get into Big Data Hadoop; hence, there is a huge demand for certified Big Data professionals. Our Big Data online training will help you learn Big Data and upgrade your career in the Big Data domain. Getting the Big Data certification from us, can put you in a different league when it comes to applying for the best jobs.

Participants in this course should have:

  • Understanding of the fundamentals of Python programming
  • Basic knowledge of statistics

Big Data Hadoop training is best suitable for IT, data management, and analytics professionals looking to acquire expertise in Big Data Hadoop, including Software Developers and Architects, Analytics Professionals, Senior IT professionals, Testing and Mainframe Professionals, Data Management Professionals, Business Intelligence Professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics.

MASTER IN BIG DATA HADOOP TRAINING & CERTIFICATION

What are the benefits of "Master in Big Data Hadoop" Certification?

The entire training course content is in line with these certification programs and helps you clear these certifth ease and get the best jobs in the top MNCs.As part of this ication exams witraining, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.Upon successful completion of the Big Data Hadoop training, you will be awarded the course completion certificate from our side.

BIG DATA HADOOP TRAINING & CERTIFICATION FAQs

The Big Data Hadoop training in Hyderabad is designed to help the candidates achieve Hadoop certification exam. It also gives a complete overview of the Big Data Framework using Hadoop and Spark. Learn to enhance your skills in using Spark for real-time data processing, including parallel processing in Spark, implementing Spark applications It is a known fact that the demand for Hadoop professionals far outstrips the supply. So, if you want to learn and make a career in Hadoop, then you need to enroll for our Hadoop course which is the most recognized name in Hadoop training and certification.Our entire Hadoop training has been created by industry professionals. You will get 24/7 lifetime support, high-quality course material and videos and free upgrade to the latest version of course material. Thus, it is clearly a one-time investment for a lifetime of benefits.

What is Hadoop :

Hadoop is an open-source framework which allows organizations to store and process big data in a parallel and distributed environment. It is used to store and combine data, and it scales up from one server to thousands of machines, each offering low-cost storage and as well as local computation

What is Hadoop :

Spark is considered by many to be a more advanced product than Hadoop. It's an open-source framework that provides several interconnected platforms, systems, and standards for big data projects.

Because We provide the best Big Data training course that gives you all the skills needed to work in the domains of Big Data, Data Science with R Statistical computing. After the completion of the training, you will be awarded the Big Data certification.You can know more about us on Web, Twitter, Facebook and linkedin and make your own decision. Also, you can email us to know more about us. We will call you back and help you more about the trusting DevOpsSchool for your online training.

You will have the skills required to help you to land a dream job. Jobs that are ideal for Big Data trained professionals . According to the Market Research, the global Hadoop market will reach $84.6 Billion by 2021 and there is a shortage of 1.4-1.9 million Hadoop data analysts in the U.S. alone. Big Data is the fastest growing and the most promising technology for handling large volumes of data for doing data analytics. This Big Data Hadoop training will help you be up and running in the most demanding professional skills. Almost all top MNCs are trying to get into Big Data Hadoop; hence, there is a huge demand for certified Big Data professionals.

You will never lose any lecture at DevOpsSchool. There are two options available: You can view the class presentation, notes and class recordings that are available for online viewing 24x7 through our Learning management system (LMS). You can attend the missed session, in any other live batch or in the next batch within 3 months. Please note that, access to the learning materials (including class recordings, presentations, notes, step-bystep-guide etc.)will be available to our participants for lifetime.

Please email to contact@DevopsSchool.com

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If you are reaching to us that means you have a genuine need of this training, but if you feel that the training does not fit to your expectation level, You may share your feedback with trainer and try to resolve the concern. We have no refund policy once the training is confirmed.

Our fees are very competitive. Having said that if the participants are in a group then following discounts can be possible based on the discussion with representative.

  • Two to Three students – 10% Flat discount
  • Four to Six Student – 15% Flat discount
  • Seven & More – 25% Flat Discount

DevOpsSchool provides "Master in Big Data Hadoop Course" certificate accredited by DevOpsCertificaiton.co which is industry recognized and does holds high value. Participant will be awarded with the certificate on the basis of projects, assignments and evaluation test which they will get within and after the training duration.

DEVOPSSCHOOL ONLINE TRAINING REVIEWS

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Abhinav Gupta, Pune

(5.0)

The training was very useful and interactive. Rajesh helped develop the confidence of all.


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Indrayani, India

(5.0)

Rajesh is very good trainer. Rajesh was able to resolve our queries and question effectively. We really liked the hands-on examples covered during this training program.


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Ravi Daur , Noida

(5.0)

Good training session about basic Devops concepts. Working session were also good, howeverproper query resolution was sometimes missed, maybe due to time constraint.


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Sumit Kulkarni, Software Engineer

(5.0)

Very well organized training, helped a lot to understand the DevOps concept and detailed related to various tools.Very helpful


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Vinayakumar, Project Manager, Bangalore

(5.0)

Thanks Rajesh, Training was good, Appreciate the knowledge you poses and displayed in the training.



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Abhinav Gupta, Pune

(5.0)

The training with DevOpsSchool was a good experience. Rajesh was very helping and clear with concepts. The only suggestion is to improve the course content.


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