Hadoop Online Training On-Demand offers comprehensive courses on a wide range of Hadoop technologies for developers, data analysts, and administrators. Designed in a format that meets your needs for convenience, availability and flexibility, these courses will lead you on the path to becoming a Certified Hadoop Professional.

Hadoop Online Training in Hyderabad A Perfect Choice for Career Growth

Hadoop’s knowledge will make a dramatic change in his career. You can increase career growth and get high pay packages with proper training and certification. Demand for Hadoop skills has increased, and IT professionals now opt for formal training in Big Data technologies. Different forecasts suggest that the upward trend in demand will continue to grow. Experts believe that proper knowledge of this technology will benefit both experienced professionals and beginners. Online training from RCP Technologies Pvt Ltd at Apache Hadoop, will help you to be a part of this movement. Our integral training highlights the core areas and their implementation in different sectors.

Training overview

The curriculum of our course is designed to meet the needs of professionals. Flexibility, availability, and amenities are three important aspects of our training format. Our training helps participants learn concepts from the Hadoop and Big Data ecosystem. You will be equipped with in-depth knowledge of managing datasets and code with the help of the framework. The curriculum of this training encompasses Hbase, Apache Drill, MapReduce and Beehive to name a few. Certified experts design the syllabus for this training. You can access our course all day throughout the year from different parts of the world.

Course Details

MKR Infotech’s Big Data Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert instructors will help you:
1. Master the concepts of HDFS and MapReduce framework
2. Understand Hadoop 2.x Architecture
3. Setup Hadoop Cluster and write Complex MapReduce programs
4. Learn data loading techniques using Sqoop and Flume
5. Perform data analytics using Pig, Hive and YARN
6. Implement HBase and MapReduce integration
7. Implement Advanced Usage and Indexing
8. Schedule jobs using Oozie
9. Implement best practices for Hadoop development
10. Work on a real life Project on Big Data Analytics
11. Understand Spark and its Ecosystem
12. Learn how to work in RDD in Spark

Who should go for this Hadoop Course?

Market for Big Data analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals.
Here are the few Professional IT groups, who are continuously enjoying the benefits moving into Big data domain:
1. Developers and Architects
2. BI /ETL/DW professionals
3. Senior IT Professionals
4. Testing professionals
5. Mainframe professionals
6. Freshers

Why learn Big Data and Hadoop?

Big Data & Hadoop Market is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015
Forbes
McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
Mckinsey Report
Avg salary of Big Data Hadoop Developers is $135k
Indeed.com Salary Data

What are the pre-requisites for the Hadoop Course?

As such, there are no pre-requisites for learning Hadoop. Knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. If you wish to brush-up Core-Java skills, MKR Infotech offer you a complimentary self-paced course, i.e. "Java essentials for Hadoop" when you enroll in Big Data Hadoop Certification course.

How will I do practicals in Online Training?

For practicals, we will help you to setup MKR Infotech's Virtual Machine in your System with local access. The detailed installation guide will be present in LMS for setting up the environment. In case, your system doesn't meet the pre-requisites e.g. 4GB RAM, you will be provided remote access to the MKR Infotech cluster for doing practical. For any doubt, the 24*7 support team will promptly assist you. MKR Infotech Virtual Machine can be installed on Mac or Windows machine and the VM access will continue even after the course is over, so that you can practice.

Where do our learners come from?

Professionals from around the globe have benefited from MKR Infotech's Big Data Hadoop Certification course. Some of the top places that our learners come from include San Francisco, Bay Area, New York, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Singapore, Australia, New Zealand, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many.

MKR Infotech’s Big Data Hadoop online training is one of the most sought after in the industry and has helped thousands of Big Data professionals around the globe bag top jobs in the industry. This online training includes lifetime access, 24X7 support for your questions, class recordings and mobile access. Our Big Data Hadoop certification also include an overview of Apache Spark for distributed data processing.
  • Week 1: Big Data and Hadoop Introduction. Hadoop HDFS and ADMINSTRATION.
  • Introduction to Big Data.
  • What is Big data
  • Big Data opportunities
  • Big Data Challenges
  • Characteristics of Big data
  • Real Time Big Data Use cases.
  • Introduction to Hadoop
  • Hadoop Distributed File System
  • Comparing Hadoop & SQL
  • Industries using Hadoop.
  • Data Locality
  • Hadoop Architecture
  • Map Reduce & HDFS.
  • Using the Hadoop single node image (Clone).
  • The Hadoop Distributed File System (HDFS) - Storage.
  • HDFS Design & Concepts
  • Blocks, Name nodes and Data nodes
  • Anatomy of File Write and Read
  • Hadoop DFS The Command-Line Interface
  • Basic File System Operations.
  • Multi Node Cluster Setup and its operations
  • More detailed explanation about Configuration files
  • Metadata, FS image, Edit log, Secondary Name Node and Safe Mode.
  • FSCK Utility. (Block report).
  • How to add New Data Node dynamically
  • HDFS High-Availability and HDFS Federation
  • How to decommission a Data Node dynamically (Without stopping cluster)
  • How to override default configuration at system level and Programming level
  • HDFS Federation
  • ZOOKEEPER Leader Election Algorithm
  • Java API for HDFS Commands.
  • Exercise and small use case on HDFS
  • Week: 2 -> CORE HADOOP (MAP-REDUCE )
  • Map Reduce
  • Functional Programming Basics.
  • Map and Reduce Basics and its Architecture
  • Anatomy of a Map Reduce Job Run
  • Legacy Architecture >Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
  • Job Completion, Failures, Shuffling and Sorting
  • Splits, Record reader, Partition, Types of partitions& Combiner
  • Hands on “Word Count” in Map/Reduce in standalone and Pseudo distribution Mode
  • Types of Schedulers and Counters
  • Getting the data from RDBMS into HDFS using Custom data types
  • Distributed Cache andHadoop Streaming (Python, Ruby and R).
  • Sequential Files and Map Files
  • Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots
  • Enabling Compressions and Compression Codec’s
  • Map side Join with distributed Cache
  • Secondary Sortings.Creating custom datatypes, comparators
  • Types of I/O Formats:Multiple outputs,NLINEinputformat.
  • Handling small files using CombineFileInputFormat
  • YARN and Hands on Practical session
  • Week: 3 -> NOSQL Basics and HBASE Database.
  • NOSQL
  • ACID in RDBMS and BASE in NoSQL
  • CAP Theorem and Types of Consistency
  • Types of NoSQL Databases in detail, Columnar Databases in Detail (HBASE and CASSANDRA).TTL, Bloom Filters and Compensation
  • HBase
  • HBase concepts
  • HBase Data Model and Comparison between RDBMS and NOSQL
  • HBase Architecture, Master & Region Servers. Block Cache and sharding
  • HBase Operations (DDL and DML) through Shell and JAVA API Programming
  • SPLITS,CATALOG TABLES
  • DATA Modeling (Sequential, Salted, Promoted and Random Keys)
  • Client Side Buffering and Process 1 million records using Client side Buffering.
  • HBASE Counters, Enabling Replication and HBASE RAW Scans
  • HBASE Filters, Bulk Loading and Coprocessors (Endpoints and Observers)
  • Real world use case consisting of HDFS, MR and HBASE.
  • HADOOP IN CLOUD
  • Introduction to AWS (Amazon Web Service).
  • Launching 4 Node Cluster in AWS and EMR
  • WEEK -4 ->HIVEWITH MYSQL.
  • HIVE
  • Introduction and Architecture
  • Hive Services, HiveShell, Hive Server and Hive Web Interface (HWI)
  • Meta store, Types of MetaStores.Configuring External Metastores.Hive QL
  • OLTP vs. OLAP
  • Working with Tables and different File Formats in HIVE
  • Primitive data types and complex data types
  • Working with Partitions, Mulitple Inserts and dynamic Partitioning
  • User Defined Functions
  • Hive Bucketed Tables and Sampling
  • External partitioned tables,Map the data to the partition in the table,Writing the output of one query to another table,Multiple inserts
  • Differences between ORDER BY, DISTRIBUTE BY and SORT BY
  • RC File. INDEXES and VIEWS.MAPSIDE JOINS
  • Compression on hive tables and Migrating Hive tables
  • Dynamic substation of Hive and Different ways of running Hive
  • How to enable Update in HIVE
  • Log Analysis on Hive
  • Access HBASE tables using Hive
  • Hands on Exercises
  • WEEK-5 -> PIG, HCATALOG and INTEGRATIONS.
  • PIG
  • Execution Types
  • Grunt Shell,Pig Latin,Data Processing,Schema on read
  • Primitive data types and complex data types
  • Tuple schema, BAG Schema and MAP Schema
  • Loading and Storing,Filtering,Grouping & Joining
  • Debugging commands (Illustrate and Explain)
  • Validations in PIG, Type casting in PIG
  • Working with Functions, User Defined Functions
  • Types of JOINS in pig and Replicated Join in detail
  • SPLITS and Multiquery execution
  • Error Handling, FLATTEN and ORDER BY
  • Parameter Substitution, Nested For Each
  • User Defined Functions, Dynamic Invokers and Macros
  • How to access HBASE using PIG
  • How to Load and Write JSON DATA using PIG
  • Piggy Bank
  • Hands on Exercises
  • IMPALA
  • Difference between Impala Hive and Pig
  • How Impala gives good performance
  • Exclusive features of Impala
  • Impala Challenges
  • Use cases of Impala
  • HCATALOG.
  • Installation
  • Introduction to HCATALOG
  • About Hcatalog with PIG,HIVE and MR
  • Hands on Exercises.
  • WEEK - 6 > SQOOP, FLUME, OOZIE and ZOOKEEPER.
  • SQOOP
  • Installation
  • Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV,Compressing,Control Parallelism, All tables Import)
  • Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
  • Free Form Query Import
  • Export data to RDBMS,HIVE and HBASE
  • Hands on Exercises
  • FLUME
  • Installation
  • Introduction to Flume.
  • Flume Agents: Sources, Channels and Sinks
  • Log User information using Java program in to HDFS using LOG4J and Avro Source
  • Log User information using Java program in to HDFS using Tail Source
  • Log User information using Java program in to HBASE using LOG4J and Avro Source.
  • Log User information using Java program in to HBASE using Tail Source
  • Flume Commands
  • Use case of Flume: Flume the data from twitter in to HDFS and HBASE. Do some analysis using HIVE and PIG
  • OOZIE
  • Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles
  • Workflow to show how to schedule Sqoop Job, Hive, MR and PIG
  • Real world Use case which will find the top websites used by users of certain ages and will be scheduled to run for every one hour.
  • SPARK
  • Basics of in memory computation
  • RDD in Spark
  • Installation
  • Spark with Scala example
  • Spark Java API
  • Spark Mlib
  • STORM BASICS
  • KAFKA BASICS

About instructors?

All our instructors are working professionals from the Industry and have at least 5-6 yrs of relevant experience in HADOOP Online Training. They are subject matter experts and are trained by MKR Infotech for providing online training so that participants get a great learning experience.

LIVE video streaming?

Yes, the classes are conducted via LIVE Video Streaming, where you can interact with the instructor. You can go through our sample class recording on this page and understand the quality of instruction and the way the class is conducted.

Backup Classes?

You can attend the missed session, in any other live batch. Please note, access to the course material will be available for lifetime once you have enrolled into the cours

Course Certification?

Yes, we provide our own Certification. At the end of your course, you will work on an HADOOP . You will receive project specifications which will help you to create an HADOOP.Once you are successfully through the project (Reviewed by an expert), you will be awarded a certificate with a performance-based grading.If your project is not approved in 1st attempt, you can take extra assistance for any of your doubts to understand the concepts better and reattempt the Project free of cost.

Practicle Sessions?

For your practical work, we will help you set-up the Java environment on your system along with HADOOP Setup. This will be a local access for you. The detailed step by step installation guides will be present in your LMS which will help you to install and set-up the environment. The support team will help you through the process.

Recorded sessions?

All your class recordings and other content like PPT’s and PDF’s etc. are uploaded on the LMS, to which you have a lifetime access.

Course Duration?

HADOOP Online Training course at MKR Infotech is an 45 hours course.

HADOOP Online Training setUp Enivoronment?

Your system should have a 4GB RAM, a processor better than core 2 duo and operating system can be of 32bit or 64 bit.

Quries?

You can give us a CALL at +91 9948382584 / 040 42036333 OR email at hr@mkrinfotech.com

MKR Infotech Certification Process:

At the end of your course, you will work on an HADOOP . You will receive project specifications which will help you to create an HADOOP.

Once you are successfully through the project (Reviewed by an expert), you will be awarded a certificate with a performance-based grading.

If your project is not approved in 1st attempt, you can take extra assistance for any of your doubts to understand the concepts better and reattempt the Project free of cost.

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