Hadoop Training


Hadoop Training in Chennai


Learn how to use Hadoop from beginner level to advanced techniques which is taught by experienced working professionals. With our Hadoop Training in Chennai you’ll learn concepts in expert level with practical manner. GREENS TECHNOLOGYS provides best Hadoop Training in Chennai as class room with placements. We designed this Hadoop Training from beginner level to advanced level and project based training with helps everyone to be ready for industry practices. Anyone who completes our Hadoop Training in Chennai will become a master in Hadoop with hands-on workouts and projects. Our Hadoop trainers are well experienced and certified working professionals with more experience in real time projects.


Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.



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Best hadoop Training in Chennai

Bigdata and Hadoop Training in Chennai for developers, data analysts and administrators delivered by certified and highly experienced trainers with extensive real-world end-to-end experience. We are the best BigData and Hadoop Developer Training institute in Chennai. Learn Hadoop, MapReduce and BigData from Scratch. A Complete Hands on training to Learn and Master the Popular Big Data Technologies with many Real World Business Use Cases.


Greens Technology Apache Hadoop training in Chennai is the expert source for Apache Hadoop training and certification. We offer public and private Hadoop courses for developers and administrators with certification for IT professionals involved in Apache Hadoop projects using Hadoop, Pig, Hive, Sqoop, HBase, Mahout, HCatalog, Flume, Cassandra, MongoDB, Redis, Solr, Lucene, Oozie, many other NoSQL Databases, and Open Source Projects.


Hadoop Big Data Training in Chennai


Get SQL, Java and Linux Course free with our Hadoop Training in Chennai !



About Instructor:
- Srikanth is a Professional Instructor & Software Architect with an exceptional history and experience of approx 11 years in leading IT projects involving complex functional as well as non-functional requirements.

Srikanth has got Hadoop Instructor accreditations from Cloudera. Srikanth worked in various domains including Big Data, eCommerce, Insurance and Location Intelligence.

Srikanth has strong background in Designing, Architecture of Enterprise applications, Java, Spring, HDFS, Map Reduce, Pig, Hive, Impala, Hadoop Administration, Google Search Appliance, Demandware eCommerce Platform, Google Analytics, Maven, Enterprise Architect, Social Integration via OAuth etc. Srikanth is actively involved in imparting trainings on various technologies including Google Search Appliance, HDFS, HBase, NoSQL, MapReduce, Hive, Pig, Impala, Java, Software Craftsmanship etc.

Srikanth has a strong expertise working on IaaS, PaaS and SaaS based environments. He has been consulting several CMM 5 organizations on best practice implementation, coding standard implementation, improve programming efficiency and code coverage, and implement automation testing and continuous integration processes.

Some of the automation tool, Srikanth has been working & consulting on includes: Cruise Control, Jenkins, Hudson. Among the collaboration software, Srikanth has an extensive experience with JIRA, both development and administration. Srikanth has got excellent exposure in developing external web applications having highly demanding non-functional requirements like Security, Performance, Usability etc.

Srikanth has positive attitude, Solution oriented approach, Constant and Fast Learner Srikanth is a graduate of Landmark education which imparts Transformative learning

Talk to the Trainer @ +91-89399 15577

What is Hadoop?


Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Hadoop is the data platform chosen by many because it provides high performance – especially if you replace MapReduce in the Hadoop stack with the Apache Spark data processing engine.
Hadoop has evolved into a user-friendly data management system. Different implementations have done their part to optimize Hadoop’s manageability through different administrative tools.
Look for a distribution that has intuitive administrative tools that assist in management, troubleshooting, job placement and monitoring.


Certified Apache Big data & Hadoop Developer training in Chennai

This course is designed for clearing Cloudera Certified Developer for Apache Hadoop (CCDH). At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Greens Technology Course Completion certificate.

This course is designed for clearing Cloudera Certified Administrator for Apache Hadoop (CCAH). At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Greens Technology Course Completion certificate.



About Hadoop Training Course in Chennai

This Combo course comprises of Hadoop Developer, Analyst, Administration & Testing.
This Combo course comes with a great discount!! Hurry up and avail the offer!!
Here are the topics covered in this course:-

Introduction to Hadoop and its Ecosystem, Map Reduce and HDFS, Deep Dive in Map Reduce and Yarn, Deep Dive in Pig, Deep Dive in Hive, Introduction to Hbase architecture, Hadoop Cluster Setup and Running Map Reduce Jobs, Major Project – Putting it all together and Connecting Dots, Advance Mapreduce, Impala, ETL Connectivity with Hadoop Ecosystem, Hadoop Cluster Configuration, Hadoop Administration and Maintenance, Hadoop Monitoring and Troubleshooting, Hadoop Multi Node Cluster Setup and Running Map Reduce Jobs on Amazon Ec2, High Availability Federation, Yarn and Security, Advance Oozie, Advance flume, Advance hue, Advance Impala, Advance zookeeper.

Hands on Exercise:

  • End to end POC using Yarn or Hadoop 2.0

Learning Objectives:

  • Programming in YARN (MRv2) latest version of Hadoop Release 2.0
  • Implementation of HBase, MapReduce Integration, Advanced Usage and Advanced Indexing.
  • Advance Map Reduce exercises – examples of Facebook sentiment analysis, LinkedIn shortest path algorithm, Inverted indexing.
  • Derive an insight into the field of Data Science
  • Understand the Apache Hadoop framework
  • Learn to work with Hadoop Distributed File System (HDFS)
  • Implement Multi node cluster using 3-4 instances of Amazon ec2.
  • Ability to design and develop applications involving large data using Hadoop eco system.
  • Differentiate between new as well as old APIs for Hadoop
  • Understand how YARN engages in managing compute resources into clusters
  • Setting up Hadoop infrastructure with single and multi node cluster on Amazon ec2 (CDH4).
  • Know best practices of using Hadoop in enterprise world
  • ETL tool connectivity with Hadoop, real time case studies etc.
  • Detailed hands on with Impala for real time queries on Hadoop.
  • Writing Hive and Pig Scripts and working with Sqoop.
  • Implementation of HBase, MapReduce Integration, Advanced Usage and Advanced Indexing.
  • Work on a Real Life Project on Big Data Analytics and gain Hands on Project Experience.
  • Implement linked-in algorithms – Identification of Shortest path for 1st level or 2nd level connection using Map Reduce.
  • Play with Datasets – Twitter data set for sentiment analysis, whether dataset, Loan Data set.
  • Guidance and Quiz to prepare for Professional Certification exams like – Cloudera, etc.
  • Ability to design and develop applications involving large data using Hadoop eco system.
  • Optimize Hadoop cluster for the best performance based on specific job requirements
  • Monitor a Hadoop cluster and execute routine administration procedures
  • Deal with Hadoop component failures and recoveries
  • Get familiar with related Hadoop projects: Hbase, Hive and Pig

Last but not the least, the Hadoop online tutorial program prepares programmers for better career opportunities in the world of Big Data.

Pre-Requisites:

Some prior experience any Programming Language would be good. Basic commands knowledge of UNIX, sql scripting. Prior knowledge of Apache Hadoop is not required.

Recommended Audience:

  • Professionals aspiring to make a career in Big Data Analytics using Hadoop Framework
  • Java Developers / Architects, BI /ETL/DW Professionals or SAS Professionals/Architects
  • Project Managers
  • Mainframe Professionals, Architects & Testing Professionals
  • Graduates aiming to build a career in Big Data

Why Go for Hadoop Training in Chennai Greens Technology's?

Hadoop is a combination of online running applications on a very huge scale built of commodity hardware. Hadoop is uncluttered source software which is handled by the Apache Software Foundation and it’s very helpful in storing and handling huge amounts of data inexpensively and professionally. Basically Hadoop collects huge packets of data and classifies this data using MapReduce.

If you are looking for Hadoop Jobs and you are a Hadoop professional then there are a lot of jobs about Hadoop and related technologies. There are many companies like Google, Yahoo, Apple, Hortonworks, eBay, Facebook, ORACLE, IBM, Microsoft, and CISCO which are looking for skilled professionals having experience in this field and are capable of managing the Big Data in their companies. If you are a professional of Hadoop then you could be one of them. These companies such as Google, Facebook, and ORACLE etc are looking for the Hadoop Professionals at different levels such as database Administrators, Hadoop Professionals having complete operational skills, Hadoop engineers & also senior Hadoop engineers, big data Engineers, Hadoop developers and also Java Engineers (DSE Team).

Research of IDC shows that the Big Data market revenue’s will grow at 31.7 percent a year and it will hit the $23.8 billion mark in 2016. According to the latest research by market the Hadoop and Big Data world widely is expected to growth about 13.9$ billion by 2017.

Companies Using Hadoop:

Amazon Web Services, IBM, Hortonworks, Cloudera, Intel, Microsoft, Pivotal, Twitter, Salesforce, AT&T Stumbleupon, Ebay, Yahoo, Facebook, Hulu etc.

Career Opportunities after Hadoop course:

Google trends tell exponential growth of Jobs in Hadoop. Check Top Job websites for Hadoop Jobs:


Best Hadoop Training Course in Chennai

In this Hadoop Training course in Chennai, you will Learn how to use Hadoop from beginner level to advanced techniques which is taught by experienced working professionals. With our Hadoop Training in Chennai you’ll learn concepts in expert level with practical manner.


Big Data &Hadoop Certification Course from Hadoop Training Chennai will help you to:


  • Get certified on “Big Data and Hadoop”
  • Master the concepts of Hadoop framework and its deployment in a cluster environment
  • Process around 3.5 Billion Data points spanning across 8 Data sets with a high level of efficiency
  • Understand Hadoop Architecture by understanding Hadoop Distribution File System operations principles (vHDFS 1.0 & vHDFS 2.0)
  • Understand advance concepts of parallel processing in MapReduce1.0 (or MRv1) & MapReduce2.0 (or MRv2)
  • Learn to write complex Map Reduce programs in both MRv1 & MRv2 (Yarn)
  • Learn high-level scripting frameworks Pig & Hive and perform data analytics using high level scripting language under Pig & Hive
  • Have a good understanding of Ecosystem and its advance components like Flume, Apache Oozie workflow scheduler etc.
  • Understand advance concepts Hadoop 2.0 : Hbase, Zookeeper and Sqoop
  • Get hands-on experience in different configurations of Hadoop cluster, its optimization & troubleshooting
  • Get a chance to work on 2 Real time Industry based Projects

Hadoop Training Course Details

Learn how to use Hadoop from beginner level to advanced techniques which is taught by experienced working professionals. With our Hadoop Training in Chennai you’ll learn concepts in expert level with practical manner. Become a Hadoop Expert by mastering MapReduce, Yarn, Pig, Hive, HBase, Oozie, Flume and Sqoop while working on industry based Use-cases and Projects.


INTRODUCTION TO BIG DATA - HADOOP

BigData(What,Why,Who) - 3++Vs-Overviews of Hadoop EcoSystem - Role of Hadoop in Big data - overviews of other Big Data System - Who is using Hadoop - Hadoop integrations into Exiting Software Products - Current Scenario in Hadoop Ecosystem - Installation - Configuration - UseCases of Hadoop(HealthCare,Retail,teecom)


HDFS

Concepts - Architecture - Data Flow(File Read,File Write) - Fault Tolerance - Shell Commands - Java Base API - Data Flow Archives - coherency - Data Integrity - Role of Secondary NameNode


MAPREDUCE

Theory - Data Flow (Map-shuffle-Reduce) - MapRed vs MapReduce APIs - Programming[Mapper,Reducer, Combiner, Partitioner] - Writable- InputFormat - Outputformat- Streaming API using python - Inherent Failure Handing using Speculative Execution - Magic of Shuffle phase - FileFormats - Sequence Files


ADVANCE MAPREDUCE PROGRAMMING

Counter(Built IN and Custom) - Custom Input Format - Distributed Cache - Joins(MapSide,FReduceSide) - Sorting - Perfomance Tuning-GenericOptionsParser - ToolRunner - Debugging(LocalJobRunner)


ADMINISTRATION

Multi Node Cluster Setup using AWS Cloud Machines - Hardware Considerations - Software Considerations - Commands(fsck,job,dfsadmin)-Schedulers in job Tracker - RackAwareness Policy - Balancing - NameNode Failure and Recovery - commissioning and Decommissioning a Node - Compression Codecs


HBASE

Introduction to NoSQL - CAP Theorem - Classification of NoSQL - Hbase and RDBMS - HBASE and HDFS - Architecture(Read Path,Write Path,Compactions,Splits) - Installation - Configuration - Role of Zookeeper - HBase Shell - Java Based APIs(Scan,Get,Other advanced APIs) - Introduction to Filter - RowKey Design - Map reduce Integration-performance Tuning - What's New in HBase0.98 - Backup and Disaster Recovery - Hands On


HIVE

Architecture - Installation - Configuration - Hive vs RDBM - Tables - DDl - DML - UDF - UDAF - Partitioning - Bucketing - MetaStore - Hive - Hbase Integration - Hive Web Interface - Hive Server(JDBC,ODBC,Thrift) - File Formats(RCFile-ORCFile) - other SQL on Hadoop


PIG

Architecture- Installation - Hive vs Pig-Pig Latin syntax - Data Types - Functions(Eval,Load/Store,String,DateTime) - joins - PigServer - Macros - UDFs -performance - Troubleshooting - Commonly Used Functions


SQOOP

Architecture - Installation,commands(Import,Hive-Import,Eval,Hbase Import,Import All tables,Export)-Connectors to Existing DBs and DW


FLUME

Why Flume?-Architecture,Configuration(Agents), Source(Exec-Avro-NetCat), Channels(File,Memory,JDBC,HBase), Sinks(Logger, Avro, HDFS, Hbase, FileRoll), Contextual Routing(Interceptors, Channel Selectors)-Introduction to other aggregation frameworks


OOZIE

Architecture, Installation, Workflow,Coordinator, Action(Mapreduce,Hive,Pig,Sqoop)-Introduction to Bundle - Mail Notifications


HADOOP 2.0

Limitations in Hadoop-1.0-HDFS Federation-High Availability in HDFS-HDFS Snapshots-Other Improvements in HDFS2-Introduction to YARN aka MR2-Limitations in MR1-Architecture of YARN-MapReduce Job Flow in YARN-Introduction to Stinger Initiative and Tez-BackWard Compatibility for Hadoop 1.X


SOLR

Introduction to Information Retrieval - common usecases- Introduction to Solr and Lucene - Installation - Concepts(Cores,Schema,Documents,fields, Inverted Index)- Configuration - CRUD Operation requests and responses - Java Based APIs - Introduction to SolrCloud


Developing Solutions Using Apache Hadoop


The Developing Solutions using Apache Hadoop training course is designed for developers who want to better understand how to create Apache Hadoop solutions. The course consists of 50% hands-on lab exercises and 50% presentation material.

After successfully completing this training course each student will receive one free voucher for the Hadoop Certified Developer exam.

Topics Covered

  • The process of taking a Hadoop project from conception to completion
  • Big Data Defined and How Hadoop makes Big Data more valuable
  • MapReduce training: Including how to write a MapReduce program using the Hadoop API
  • HDFS training: Including effective loading and processing of data with CLI and API
  • Pig, Hive and Hcatalog: how to accomplish Data movement and processing with higher level languages
  • Over 15 hands-on training exercises using HDFS, Pig, Hive, HBase, key MapReduce components and features (e.g. mapper, reducer, combiner, partitioner and streaming) and more

Intended Audience

  • Java is preferred, but experience in other programming languages such as PHP,C,C+ is sufficient

Course Duration

  • The Apache Hadoop Developer class is a four-day course spanning 26 total hours
  • There are approximately 13 hours each of hands-on labs and presentations

Hadoop Architect Course Content


    Introduction to Distributed systems
  • High Availability
  • Scaling
  • Advantages
    • Introduction to Big Data
  • Big Data opportunities
  • Big Data Challenges
    • Introduction to Hadoop
  • Hadoop Distributed File System
  • Hadoop Architecture
  • Map Reduce & HDFS
    • Hadoop Eco Systems
  • o Introduction to Pig
  • Introduction to Hive
  • Introduction to HBase
  • Other eco system Map
    • Hadoop Administration
  • Hadoop Installation & Configuration
  • Setting up Standalone system
  • Setting up pseudo distributed cluster
  • Setting up distributed cluster
    • The Hadoop Distributed File System (HDFS)
  • HDFS Design & Concepts
  • Blocks, Name nodes and Data nodes
  • Hadoop DFS The Command-Line Interface
  • Basic File System Operations
  • Reading Data from a Hadoop URL
  • Reading Data Using the File System API
    • Map Reduce
  • Map and Reduce Basics.
  • How Map Reduce Works
  • Anatomy of a Map Reduce Job Run
  • Job Submission, Job Initialization, Task Assignment, Task Execution
  • Progress and Status Updates
  • Job Completion, Failures
  • Shuffling and Sorting.
  • Combiner
  • Hadoop Streaming
    • Map/Reduce Programming – Java
  • Hands on “Word Count” in Map/Reduce in Eclipse
  • Sorting files using Hadoop Configuration API discussion
  • Emulating “grep” for searching inside a file in Hadoop
  • Chain Mapping API discussion
  • Job Dependency API discussion and Hands on
  • Input Format API discussion and hands on
  • Input Split API discussion and hands on
  • Custom Data type creation in Hadoop


  • Developing Apache Hadoop Applications in Java


    This 30hrs hands-on training course takes a deep-dive into developing Java MapReduce applications for Big Data deployed on the Hadoop Distributed File System (HDFS). Students who attend this course will learn how to harness the power of Apache Hadoop™ and MapReduce to manipulate, analyze and perform computations on their Big Data.

    Prerequisites

    This course assumes students have experience developing Java applications and using a Java IDE. Labs are completed using the Eclipse IDE and Maven.

    Target Audience

    Experienced Java developers responsible for developing MapReduce applications and performing analysis of Big Data stored on Apache Hadoop.

    Course Objectives

    At the completion of the course, student will be enabled to perform the following:

    • Write a Java MapReduce application using Eclipse and Maven
    • Develop a Combiner to perform map aggregation
    • Customize input and output formats of a MapReduce job
    • Compute mathematical computations on your Big Data files
    • Use best practices to optimize MapReduce jobs
    • Create JUnit tests for a MapReduce job
    • Discover trends in your Big Data
    • Define an Oozie workflow
    • Access Apache HBase™ data from a Java MapReduce job
    • Write custom, user-defined functions for Apache Pig™ and Apache™ Hive

    Lab Content

    Students will work through the following exercises using Eclipse, Maven and the Hortonworks Data Platform:

    • Configuring a Hadoop ™ Development Environment
    • Word Count
    • Distributed Grep
    • Inverted Index
    • Using a Combiner
    • Computing an Average
    • Writing a Custom Partitioner
    • Using a TotalOrderPartitioner
    • Custom Sorting
    • Writing a Custom InputFormat
    • Customizing Output
    • Simple Moving Average
    • Using Data Compression
    • Defining a RawComparator
    • A Map-Side Join
    • Using a Bloom Filter
    • Unit Testing
    • Defining an Oozie Workflow
    • Term Frequency–Inverse Document Frequency (TF-IDF)
    • Accessing HBase from Java MapReduce
    • Writing a User-Defined Pig Function
    • Writing a User-Defined Hive Function

    Administering Apache Hadoop


    This three-day Apache Hadoop training course is designed for administrators who deploy and manage Apache Hadoop clusters. This course will walk you through installation, provisioning and ongoing resource management within a Hadoop cluster. You will leave with a valuable set of best practices that have been developed over years of working with Hadoop in production so that you can optimize your Hadoop clusters.

    We’ll work through the full lifecycle of an Apache Hadoop deployment using realistic hands-on lab experiments. You’ll learn from our experience over the past six years of working with some of the largest Hadoop clusters in the world.

    Duration | Objectives | Audience | Course Outline

    Duration

    The Hadoop Administration course spans three days and provides a solid foundation for management of your Hadoop clusters. A full outline is below.

    Objectives

    In this course you will learn the best practices for Apache Hadoop administration as experienced by the developers and architects of core Apache Hadoop.

    • How to size and deploy a cluster
    • How to deploy a cluster for the first time
    • How to perform ongoing maintenance to nodes in the cluster
    • How to balance and performance tune a cluster
    • How to integrate status and health checks into your existing monitoring tools (single plane of glass)
    • How to recover form a NameNode or DataNode failure
    • How to implement a high available solution
    • Best practices for deploying Hadoop clusters

    Audience

    This course is designed for IT administrators and operators with at least basic knowledge of Linux. Existing knowledge of Hadoop is not required.

    Course Outline

    Day 1: Deployment: Sizing, deployment and provisioning

    • Introduction to Hadoop
    • Best Practices for Hadoop Cluster Hardware and Software
    • Basic Hadoop Operations
    • Installing Hadoop using HMC and Ambari
    • Benchmarking Hadoop
    • Creating a multi-user environment in Hadoop
    • Understanding logs and directory structures in Hadoop

    Day 2: Management: Management, monitoring and high availability

    • Understanding configuration files
    • Monitoring the cluster with Nagios and Ganglia
    • Understanding dfsadmin and mradmin
    • Understanding Schedulers in Apache Hadoop
    • Data Integrity with Apache Hadoop
    • Using Rack Topology

    Day 3: Maintenance

    • Commissioning and Decommissioning nodes
    • NameNode Back up and Recovery
    • Hadoop Security
    • Copying Cluster Data
    • Hadoop Archive
    • Upgrading Hadoop
    • Hadoop Ecosystem
    • Oozie Administration
    • Hcatalog and Hive Administration

    About Hadoop Testing Training Course in Chennai

    This Hadoop Testing Training in Chennai is to make you enable to learn and understand to test and rectify errors from Hadoop projects performance. This Hadoop Testing Training Course in Chennai will enable you to learn about Hadoop Software, Hadoop Architecture, HDFS, Mapreduce Jobs, Hive, PIG, POC and lab exercise.

    Learning Objectives:

    • Setting up Hadoop infrastructure with single and multi node cluster on Amazon ec2 (CDH4).
    • Writing Hive and Pig Scripts and working with Sqoop.
    • Work on a Real Life Project on Big Data Testing and gain Hands on Project Experience.
    • Guidance and Quiz to prepare for Professional Certification exams like – Cloudera, etc.
    • Ability to design and test Hadoop applications involving large data using MRUnit testing Framework.

    Recommended Audience:

    This course is recommended for:

    • QA Professionals aspiring to make a career in Big Data Analytics using Hadoop Framework.
    • System Administrators and Support Engineers who will test Hadoop works

    Pre-Requisites:

    • Basic knowledge of QA
    • Basic knowledge of Unix, sql scripting
    • Prior knowledge of Apache Hadoop is not required

    Why Go for Hadoop Testing Training?

    Hadoop is a combination of online running applications on a very huge scale built of commodity hardware. Hadoop is uncluttered source software which is handled by the Apache Software Foundation and it’s very helpful in storing and handling huge amounts of data inexpensively and professionally. Basically Hadoop collects huge packets of data and classifies this data using MapReduce.

    If you are looking for Hadoop Jobs and you are a Hadoop professional then there are a lot of jobs about Hadoop and related technologies. There are many companies like Google, Yahoo, Apple, Hortonworks, eBay, Facebook, ORACLE, IBM, Microsoft, and CISCO which are looking for skilled professionals having experience in this field and are capable of managing the Big Data in their companies. If you are a professional of Hadoop then you could be one of them. These companies such as Google, Facebook, and ORACLE etc are looking for the Hadoop Professionals at different levels such as database Administrators, Hadoop Professionals having complete operational skills, Hadoop engineers & also senior Hadoop engineers, big data Engineers, Hadoop developers and also Java Engineers (DSE Team).

    Research of IDC shows that the Big Data market revenue’s will grow at 31.7 percent a year and it will hit the $23.8 billion mark in 2016. According to the latest research by market the Hadoop and Big Data world widely is expected to growth about 13.9$ billion by 2017.

    Hadoop QA Professional: Hadoop QA professional is a person who tests and rectifies glitches in Hadoop and its Data base system.

    Companies Using Hadoop:

    Amazon Web Services, IBM, Hortonworks, Cloudera, Intel, Microsoft, Pivotal, Twitter, Salesforce, AT&T Stumbleupon, Ebay, Yahoo, Facebook, Hulu etc.


    About Hadoop Analyst Training in Chennai

    Hadoop Data Analyst Training: Using Pig, Hive, and Impala with Hadoop. This Hadoop Analyst Hands-On Training course in Chennai from Greens Technology, focusing on Apache Pig, Apache Hive, and Cloudera Impala, will teach you to access, manipulate, and analyze massive data sets in your Hadoop cluster. Using SQL and a simple scripting language you will learn how to perform ETL tasks and gain valuable insight from your data.


    • Introduction
    • Hadoop Fundamentals
    • Introduction to Pig
    • Basic Data Analysis with Pig
    • Processing Complex Data with Pig
    • Multi-Dataset Operations with Pig
    • Pig Troubleshooting and Optimization
    • Introduction to Impala and Hive
    • Querying with Impala and Hive
    • Data Management
    • Data Storage and Performance
    • Relational Data Analysis With Impala and Hive
    • Working with Impala
    • Analyzing Text and Complex Data with Hive
    • Hive Optimization
    • Extending Hive
    • Choosing the Best Tool for the Job
    • Conclusion


    Developing Solutions for Apache Hadoop on Windows


    Students will learn to develop applications and analyze big data stored in Apache Hadoop running on Microsoft Windows. Students will learn the details of the Hadoop Distributed File System (HDFS ™) architecture and MapReduce framework, as well as learn how to develop applications on Hadoop® using tools like C#, Pig , Hive, HCatalog, Sqoop, Oozie and Microsoft Excel.

    Duration

    4 days

    Prerequisites

    Students should have programming experience, preferably with Visual Studio and SQL, as well as familiarity with the Windows Server operating system. No prior Hadoop knowledge is required.

    Target Audience

    .NET Developers and Data Analysts responsible for developing applications and performing analysis on big data using the Hortonworks Data Platform for Windows.

    Course Objectives

    At the completion of the course students will be able to:

    • Explain the various tools and frameworks in the Hadoop ecosystem
    • Recognize use cases for HDP for Windows and Big Data
    • Explain the architecture of the Hadoop Distributed File System (HDFS)
    • Transfer data between HDFS and Microsoft SQL Server using Sqoop
    • Explain the architecture of MapReduce
    • Run a MapReduce job on Hadoop
    • Use Hadoop streaming
    • Use the Microsoft .NET API for Hadoop to write a C# MapReduce job
    • Recognize use cases for Pig
    • Write a Pig script to explore and transform data in HDFS
    • Define advanced Pig relations
    • Use Pig to apply structure to unstructured Big Data
    • Join large datasets using Pig
    • Invoke a Pig User-Defined Function
    • Write a Hive query using Hive QL
    • Understand how Hive tables are defined and implemented
    • Use Hive to run SQL-like queries to perform data analysis
    • Explain the uses and purpose of HCatalog
    • Use an HCatalog schema within a Pig script
    • Explain the purpose of the Hive ODBC driver
    • Connect Microsoft Excel to HDFS using Hive ODBC
    • Import Hive query results into Excel
    • Explain the usages of Oozie
    • Write and execute an Oozie workflow

    Lab Content

    Students will work through the following lab exercises using the Hortonworks Data Platform for Windows:

    • Access HDFS using the HDFS commands
    • Import SQL Server data into HDFS using Sqoop
    • Export HDFS data from HDFS into SQL Server using Sqoop
    • Run a MapReduce Job
    • Monitor a MapReduce Job
    • Develop a .NET MapReduce application in C#
    • Explore data using Pig
    • Split and join datasets using Pig
    • Transform unstructured for use with Hive
    • Analyze Big Data with Hive
    • Understanding MapReduce with Hive
    • Joining datasets with Hive
    • Use HCatalog with Pig
    • Use Hive ODBC with Microsoft Excel
    • Define an Oozie Workflow

    What is Apache Hadoop?


    Hadoop is an open source project from Apache that has evolved rapidly into a major technology movement. It has emerged as the best way to handle massive amounts of data, including not only structured data but also complex, unstructured data as well. Its popularity is due in part to its ability to store, analyze and access large amounts of data, quickly and cost effectively across clusters of commodity hardware.

    Apache Hadoop is not actually a single product but instead a collection of several components including the following:

    MapReduce – A framework for writing applications that processes large amounts of structured and unstructured data in parallel across large clusters of machines in a very reliable and fault-tolerant manner.

    Hadoop Distributed File System (HDFS) – A reliable and distributed Java-based file system that allows large volumes of data to be stored and rapidly accessed across large clusters of commodity servers.

    Hive – Built on the MapReduce framework, Hive is a data warehouse that enables easy data summarization and ad-hoc queries via an SQL-like interface for large datasets stored in HDFS.

    Pig – A platform for processing and analyzing large data sets. Pig consists on a high-level language (Pig Latin) for expressing data analysis programs paired with the MapReduce framework for processing these programs.

    HBase – A column-oriented NoSQL data storage system that provides random real-time read/write access to big data for user applications.

    ZooKeeper – A highly available system for coordinating distributed processes. Distributed applications use ZooKeeper to store and mediate updates to important configuration information.

    Ambari – An open source installation lifecycle management, administration and monitoring system for Apache Hadoop clusters.

    HCatalog – A table and metadata management service that provides a centralized way for data processing systems to understand the structure and location of the data stored within Apache Hadoop.

    Apache Hadoop is generally not a direct replacement for enterprise data warehouses, data marts and other data stores that are commonly used to manage structured or transactional data. Instead, it is used to augment enterprise data architectures by providing an efficient and cost-effective means for storing, processing, managing and analyzing the ever-increasing volumes of semi-structured or un-structured data being produced daily.

    Apache Hadoop can be useful across a range of use cases spanning virtually every vertical industry. It is becoming popular anywhere that you need to store, process, and analyze large volumes of data. Examples include digital marketing automation, fraud detection and prevention, social network and relationship analysis, predictive modeling for new drugs, retail in-store behavior analysis, and mobile device location-based marketing.

    Apache Hadoop is widely deployed at including many of the world’s leading Internet and social networking businesses. At Yahoo!, Apache Hadoop is literally behind every click, processing and analyzing petabytes of data to better detect spam, predicting user interests, target ads and determine ad effectiveness. Many of the key architects and Apache Hadoop committers from Yahoo! founded Hortonworks to further accelerate development and adoption and assist organizations achieve similar business value.

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    Greens Technologys Overall Reviews


    Greens Technologys Overall Reviews 4.9 out of 5 based on 17 ratings. 17 user reviews.
    hadoop training chennai """I think this is the best hadoop training course in chennai I have taken so far..Well I am still in the process of learning new things but for me this learning process has become so easy only after I joined this course in greens technologies..as Srikanth is very organized and up to the point.. he knows what he is teaching and makes his point very clear by explaining numerous times. I would definitely recommend anyone who has any passion for hadoop.." ""