Big-Data Hadoop Developer Training

  Date :
18 May - 16 Jun
(10 days)
  time :
07:00 AM - 10:00 AM IST
price :
21,999.00

DESCRIPTION

ABOUT THE COURSE

COURSE DESCRIPTION

Big data Hadoop Developer training at root2learns helps you to be proficient in all the core concepts of Hadoop Framework like Map-reduce, HBASE, PIG, SQOOP, HIVE, Oozie, Impala, Apache Spark, Flume. Hadoop is the technology that handles Big Data processing efficiently and helps in storing, handling, and retrieving a large amount of data in different applications.

At Root2learn, we have expert trainers who will provide the best Big Data Hadoop Training and make you learn every element of it. We also provide Big Data Hadoop Training online for the people who cannot attend the classroom sessions.

Why learn Big Data and Hadoop?

Hadoop makes the impossible things happen by storing enormous data of the business at affordable price. The need for processing the big data which is in unstructured form is increasing and so the demand for Hadoop requirement. It may be difficult to learn Hadoop, but not when the professionals of Root2learn help you with Big Data Hadoop training online.

Soon, it is going to become a need for many big data companies to learn Hadoop in order to solve the problems of large data. So, Hadoop should be a must-have skill in your resume to become a professional in data market. At Root2learn, we help you learn the subject to the core with both theoretical and practical sessions.

According to the Peer Research, more than 77 percent of companies consider Hadoop as a mandatory skill. McKinsey Global Report says that there is a requirement of about 2 million Big Data managers and the average salary could be $135k. So, it is a must to join Big Data Hadoop training online and we assure that Root2learn is the best choice for it.

    1. Introduction to Linux and Big Data Virtual Machine ( VM)
      Introduction/ Installation of VirtualBox and the Big Data VM Introduction to Linux Why Linux?
      Windows and the Linux equivalents  Different flavors of Linux  Unity Shell (Ubuntu UI) Basic Linux
      Commands (enough to get started with Hadoop)
    2. Understanding Big Data
      3V ( Volume- Variety- Velocity) characteristics
      Structured and Unstructured Data
      Application and use cases of Big Data
      Limitations of traditional large Scale system s
      How a distributed way of computing is superior (cost and scale) Opportunities and challenges with Big Data
    3. HDFS (The Hadoop Distributed File System)
      HDFS Overview and Architecture
      Deployment Architecture
      Nam e Node, Data Node and Checkpoint
      Node ( aka Secondary Name Node)
      Safe mode
      Configuration files
      HDFS Data Flows ( Read v/s Write)
    4. How HDFS addresses fault tolerance?
      CRC Check Sum
      Data replication
      Rack awareness and Block placement policy
      Small files problem
    5. HDFS Interfaces
      Command Line Interface
      File System
      Administrative
      Web Interface
    6. Advanced HDFS features
      Load Balancer
      Dist Cp
      HDFS Federation
      HDFS High Availability
      Hadoop Archives
    7. Map Reduce 1 (Theoretical Concepts)
      MapReduce overview
      Functional Programming paradigm s
      How to think in a MapReduce way?
    8. MapReduce Architecture
      Legacy MR v/s Next Generation MapReduce
      ( aka YARN/ MRv2)
      Slots v/s Containers
      Schedulers
      Shuffling, Sorting
      Hadoop Data Types
      Input and Output Formats
      Input Splits  Partitioning ( Hash Partitioner
      v/s Customer Partitioner)
      Configuration files
      Distributed Cache
    9. MR Algorithm and Data Flow
      Word Count
    10. Alternatives to MR BSP (Bulk Synchronous Parallel)
      Adhoc querying
      Graph Computing Engines
    11. Map Reduce 2 (Practice) Developing, debugging and deploying MR programs
      Stand alone mode ( in Eclipse)
      Pseudo distributed mode ( as in the Big
      Data VM)
      Fully distributed mode ( as in Production)
      MR API
      Old and the new MR API
      Java Client API
      Hadoop data types and custom Writable
    12. WritableCom parables
      Different input and output formats
      Saving Binary Data using SequenceFiles
      and Avro Files
      Hadoop Streaming (developing and
      debugging non Java MR program s  Ruby
      and Python)
    13. Optimization techniques
      Speculative execution
      Combiners
      JVM Reuse
      Compression
    14. MR algorithms (Non- graph)
      Sorting
      Term Frequency
      Inverse Document Frequency
      Student Data Base
      Max Temperature
      Different ways of joining data
      Word Co- Occurrence
    15. MR algorithms (Graph)
      PageRank
      Inverted Index
    16. Higher Level Abstractions for MR (Pig)
      Introduction and Architecture
      Different Modes of executing Pig constructs
      Data Types
      Dynamic invokers Pig streaming Macros
      Pig Latin language Constructs (LOAD, STORE,
      DUMP, SPLI T, etc) User Defined Functions
      Use Cases
    17. Higher Level Abstractions for MR (Hive)
      Introduction and Architecture
      Different Modes of executing Hive queries
      Metastore Implementations
      HiveQL (DDL & DML Operations) External v/s
      Managed Tables Views
      Partitions & Buckets User Defined Functions
      Transformations using Non Java Use Cases
    18. Comparison of Pig and Hive
      NoSQL Databases 1 (Theoretical
      Concepts)
      NoSQL Concepts
      Review of RDBMS
      Need for NoSQL
      Brewers CAP Theorem
      ACI D v/s BASE
      Schema on Read vs. Schema on Write
      Different levels of consistency
      Bloom filters
    19. Different types of NoSQL databases
      Key Value
      Columnar
      Document
      Graph
    20. Columnar Databases concepts NoSQL Databases 2 (Practice)
      HBase Architecture
      Master and the Region Server
      Catalog tables ( ROOT and META)
      Major and Minor compaction
      Configuration files
      HBase v/s Cassandra
    21. Interfaces to HBase (for DDL and DML operations)
      Java API
      Client API
      Filters
      Scan Caching and Batching
      Command Line Interface
      REST API
    22. Advance HBase Features
      HBase Data Modeling
      Bulk loading data in HBase
      HBase Coprocessors EndPoints (similar to Stored Procedur es in RDBMS)
      HBase Coprocessors Observers (similar to Triggers in RDBMS)
    23. Spark
      Introduction to RDD
      Installation and Configuration of Spark
      Spark Architecture
      Different interfaces to Spark
      Sample Python program s in Spark
    24. Introduction to YARN
      Usecase of YARN
      YARN Architecture
      YARN Demo
    25. Introduction to Oozie
      Usecase of Oozie
      Oozie Architecture
      Oozie Demo
    26. Introduction to Flume
      Usecase of Flume
      Flume Architecture
      Flume Demo
    27. Introduction to Sqoop
      Usecase of Sqoop
      Sqoop Architecture
      Sqoop Demo
    28. Setting up a Hadoop Cluster using Apache Hadoop
      Cloudera Hadoop cluster on the Amazon Cloud (Practice)
      Using EMR ( Elastic Map Reduce)
      Using EC2 ( Elastic Compute Cloud)
    29. SSH Configuration
      Stand alone mode (Theory) Distributed mode (Theory)
      Pseudo distributed
      Fully distributed
    30. Hadoop Ecosystem and Use Cases
      Hadoop industry solutions
      Importing/ exporting data across RDBMS and
      HDFS using Sqoop Getting real- time events
      into HDFS using Flume
      Creating workflows in Oozie Introduction to
      Graph processing Graph processing with
      Neo4J
      Using the Mongo Document Database
      Using the Cassandra Columnar Database
      Distributed Coordination with ZooKeeper
    31. Proof of concepts and use cases
      Click Stream Analysis using Pig and Hive
      Analyzing the Twitter data with Hive
      Further ideas for data analysis

 Benefits of Root2learn Online big Data Hadoop Training:

There are numerous benefits of taking Big Data and Hadoop certification training. As the list is really big, we are going to mention a few here to make you understand the importance of Big Data Hadoop Training.

Classroom and Online Training:

At Root2learn, we offer both classroom and online training and the people who want to join our Big Data and Hadoop Certification training can opt as per their convenience. Our professionals will train you to analyze and develop Big Data computations just like any other experienced Hadoop developer. As we allow only a few members per batch to join our training, we have enough time to focus on every single member who comes to us.

Practice:

We believe practice can make anyone confident and we will help you practising every chapter after the theoretical session which is highly helpful in getting your dream job. After the completion of training, we will make you well-prepared for the examination with our secret exam tips that no other training centre will reveal to the students.

Java for Free:

To learn Big Data Hadoop, you must have knowledge on Java. So we are providing access to course ‘Java essentials for Hadoop’ for our students for Free. This is really a wonderful offer that anyone shouldn’t miss!

Better Career Opportunities:

Many top companies are looking for experienced professionals who have sound knowledge on Hadoop Framework like PIG, HBASE, SQOOP, HIVE, Map-reduce. The requirement for Hadoop professionals is increasing in various industries, such as Healthcare, Sports, Agriculture, and Media.

At Root2learn, we offer Big Data and Hadoop Developer Certification Program and the people who trained by us can apply for the posts of Hadoop Architect, Hadoop Developer, Data Scientist, Project manager, Business Intelligence Professionals, Big Data Analytics Professionals, and Hadoop administrator.

Better Salary:

As there is a large requirement and less number of Hadoop Developers, Big Data companies are hiring the Hadoop professionals by offering them high salaries. Though you are a fresher and have good practical knowledge on Hadoop, there are more chances to get a job with high salary.

Who can attained:

Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
  • Software Developers and Architects
  • Analytics Professionals
  • Data Management Professionals
  • Business Intelligence Professionals
  • Project Managers
  • Aspiring Data Scientists
  • Graduates looking to build a career in Big Data Analytics
  • Anyone interested in Big Data Analytics
Prerequisite: Knowledge of Java is necessary for this course, so we are providing complimentary access to Java Essentials for Hadoop along with the course.

Hadoop is one of the most important certifications of Big Data and the companies who want to hire Hadoop Developers will definitely check for the certification. There is a real shortage of the Hadoop developers who have real-time knowledge on Big Data and Hadoop. This is the reason why we suggest you to consider taking Big Data and Hadoop Certification training.

At Root2learn, we are offering Big Data and Hadoop Developer Certification Program with all facilities for the affordable price. As we have certified Hadoop trainers, we ensure that you will gain expertise in Hadoop development at the end of the training.

This course will be useful for professional in the following roles, among others:
  • Associate Project Managers
  • Project Managers
  • IT Project Managers
  • Project Coordinators
  • Project Analysts
  • Project Leaders
  • Senior Project Managers
  • Team Leaders
  • Product Managers
  • Program Managers
  • Project Sponsors
  • Project Team Members seeking PMP or CAPM certification.

How do you provide online training ?

The training would be provided over a web platform. It is the most demanded & modernized way of “Instructor Led Training” without the need for expensive travelling that can be attended from anywhere in the world. You can attained from your home.

Big-Data Hadoop Developer Training
4.82









11 Reviews

Which option do I choose for training, Virtual or classroom training?

You can decide which one suitable for you:

Virtual classroom Classroom
Less Expensive More Expensive
Recorded video of same session to refer in future No, recorded video
Can attain from any place, internet ( 512 KBPS speed) and System required Need to go at training venue
Can attain from home or office or from other country No, have to stay in same city
Interactive session Interactive session
Interaction with global professionals Mostly local professionals
Flexi class pass, can attain as many class want in same fee One class
If miss any class can go through same training video to connect in next session, and ask if have any query or can attain in any batch If miss the class, will not able to attain same session
Gradually learning ( as training will go near about one month, so you can prepare with training) will get enough time to revise covered topics Some training will finished in 4 days, or within one week. So it will be more load and will not have enough time to revise covered topics
Highly expected trainer ( 23 years, 6 years training experience) May be have experienced trainer
Demo session ( past recorded video) Not available
Big-Data Hadoop Developer Training
4.82









11 Reviews

What is Virtual classroom training?

Virtual classroom training for Big data and Hadoop is training conducted via online live streaming of a class. The classes are conducted by a Certified trainer with more than 20 years of work and training experience. It is interactive session, you can asked the question to trainer and will also ask the question. it is one to one interaction. It is video conference type of training.

Big-Data Hadoop Developer Training
4.82









11 Reviews

Is this live training, or will I watch pre-recorded videos?

All the classes are live. They are interactive sessions that enable you to ask questions and participate in discussions during the class time. We do, however, provide recordings of each session you attend for your future reference.

Big-Data Hadoop Developer Training
4.82









11 Reviews

What tools do I need to attend the training sessions?

The tools you’ll need to attend training are fairly basic:
  • Windows: any version newer than Windows XP SP3
  • Mac: any version newer than OSX 10.6
  • Internet speed: Preferably faster than 512 Kbps
  • Headset, speakers, microphone: You’ll need headphones or speakers to hear clearly, as well as a microphone to talk to the others. You can use a headset with a built-in microphone, or separate speakers and microphone.
Big-Data Hadoop Developer Training
4.82









11 Reviews

Where is the training held?

There is no training venue for Virtual classroom training. It is online live training you can  attained from your home by login at your system, for that we will provide you login id and password.
For classroom training you will get email at your registered email id as per your location.

Big-Data Hadoop Developer Training
4.82









11 Reviews

What is 100% training quality guarentee?

If you are not happy with our training quality, inform us within 1st half of Training on First Day. We will refund your entire training fee with 7 working days.

Big-Data Hadoop Developer Training
4.82









11 Reviews

Big-Data Hadoop Developer Training
4.82
11 Reviews

11 Comments

  1. Pradeep Amin April 4, 2017
  2. Ashu Kapre April 4, 2017
  3. Sankaragallu Visweswarappa April 4, 2017
  4. Kiran Rao April 4, 2017
  5. Ashish Mishra April 4, 2017
  6. Yatish Arun Patil April 4, 2017
  7. Nagaraj Srinivasa April 4, 2017
  8. Sridhar Bandi April 4, 2017
  9. Radhika Bandi April 4, 2017
  10. Abdul Rahiman Pasha April 4, 2017
  11. Rejith K April 4, 2017

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