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hadoop training

Best Hadoop Training Institute | Leading Institute for Hadoop Training

No doubt Sky InfoTech is one of the best HADOOP training institute in Noida. We have the best MNCs trainer who provides the best practical knowledge during HADOOP course through their years of experience and live projects so that students gain professional skills and easy to hired by the best MNCs.  Sky InfoTech design HADOOP course as per the latest technologies and development in the Industry and their requirement.

Sky InfoTech is the reliable and consistence HADOOP training institute in Noida with their 100% placement assistance. We provides excellent learning environment, well-furnished infrastructure and advance lab facilities which make our institute is one of the best in Noida. The flexibility with the time in Sky InfoTech is the best part, we conduct our classes every day, at weekend, in anytime of the day and also we train with fast-track classes. Sky InfoTech has a dedicated placement team, which provides mandatory placement training which make the student enough capable to face the interview challenges at the time of recruitment.

Syllabus of HADOOP course in the Sky InfoTech has been customizable in such a way to basic to advance. It design by keeping in mind the latest development and requirement of industry. The content of our institute of HADOOP course is fulfill the requirement of both the professional and the beginner as well. We will start what is HADOOP and tell about every tinny information in our live project class. We will talk about cloud services, provisioning resources, storing data, simplifying the database infrastructure and much more that make you professional to achieve your career goals.

About Hadoop

Apache Hadoop is open-source software. It is used for storing data and running applications on a bunch of commodity hardware. This framework is written in JavaScript programming language which allows distributed processing of large datasets across the bunch of computers using simple programming models.

Hadoop Apache is designed to scale up from one server to Hundreds of machines, each offering storage, and computation.

Hadoop Architecture

 Apache Hadoop divided into two major layer-

  • Storage layer (Hadoop Distributed File System)
  • Processing/Computation layer (MapReduce)

Storage Layer-

The Hadoop Distributed File System (HDFS) is the storage system that is primary and used by the Hadoop application. It is designed to run on commodity hardware based on the Google File System. It designed as deployed on low-cost hardware and it is highly fault-tolerant. There are significant differences from other distributed file systems. It provides a DataNode and NameNode architecture to execute a distributed file system that provides high-performance access to data over highly scalable Hadoop clusters.

There are two components more in Hadoop storage layer-

  • Hadoop YARN − This is a schedule of activities and management of team resources.
  • Hadoop Common – This is Java library and utilities required by other Hadoop modules.

MapReduce

MapReduce is a programming model designed for processing large volumes of data alike by dividing the task into a set of independent functions. It is also called a processing layer of Hadoop. You just need to place business logic the way MapReduce works, and then the framework can handle other transactions.

Work of Hadoop

 To build larger servers with heavy configurations is quite expensive that handle large-scale processing, but as an alternative, you can connect many computers to a single CPU, as a single, distributed system, and virtually group machines can read the data set in parallel and deliver much faster speeds. Plus, it’s cheaper than a tall server. So this is a motivating factor behind the use of Hadoop, which uses low-cost machines.

Hadoop can runs code over the bunch of computers. There are many tasks which Hadoop work-

  • Data is created in a separate file. Files are written in a 128M and 64M format (preferably 128M).
  • HDFS, at the top of the local file system, monitors processing.
  • Boxes serve as a benchmark for the use of emergency equipment.
  • Code execution successfully executed.
  • Performing the type that takes place between the map and reducing the degrees.
  • Send sorted data to specific computers.
  • Create debugging logs for each task.

 

Advantages of Hadoop

 There are many Advantages of Hadoop Apache-

  • Hadoop framework allows users to quickly build and test distributed systems. Efficient, automatically distributes data, works across multiple systems, and leverages the native parallelism of the CPU cores.
  • Hadoop is not ashamed of slave devices to provide fault tolerance and high availability (FTHA). It’s better to detect himself up in the library are not designed for Hadoop, as the inscription is utilizing the application of the layer, and that the breaches began to be.
  • Servers can be dynamically added or removed from the bunch, and Hadoop continues to operate continuously.
  • Another great advantage of Hadoop is that, in addition to being open-source, it is compatible with all platforms, as it is based on Java.

1. Module 1

Hadoop Architecture

Learning Objectives – In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem, How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.

Topics – What is Big Data, Hadoop Architecture, Hadoop ecosystem components, Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework, Hadoop Server Roles: NameNode, Secondary NameNode, and DataNode, Anatomy of File Write and Read.

 

2. Module 2

Hadoop Cluster Configuration and Data Loading

Learning Objectives – In this module, you will learn the Hadoop Cluster Architecture and Setup, Important Configuration files in a Hadoop Cluster, Data Loading Techniques.

Topics – Hadoop Cluster Architecture, Hadoop Cluster Configuration files, Hadoop Cluster Modes, Multi-Node Hadoop Cluster, A Typical Production Hadoop Cluster, MapReduce Job execution, Common Hadoop Shell commands, Data Loading Techniques: FLUME, SQOOP, Hadoop Copy Commands, Hadoop Project: Data Loading.

 

3 .Module 3

Hadoop MapReduce framework

Learning Objectives – In this module, you will understand Hadoop MapReduce framework and how MapReduce works on data stored in HDFS. Also, you will learn what are the different types of Input and Output formats in MapReduce framework and their usage.

Topics – Hadoop Data Types, Hadoop MapReduce paradigm, Map and Reduce tasks, MapReduce Execution Framework, Partitioners and Combiners, Input Formats (Input Splits and Records, Text Input, Binary Input, Multiple Inputs), Output Formats (TextOutput, BinaryOutPut, Multiple Output), Hadoop Project: MapReduce Programming.

 

4. Module 4

Advance MapReduce

Learning Objectives – In this module, you will learn Advance MapReduce concepts such as Counters, Schedulers, Custom Writables, Compression, Serialization, Tuning, Error Handling, and how to deal with complex MapReduce programs.

Topics – Counters, Custom Writables, Unit Testing: JUnit and MRUnit testing framework, Error Handling, Tuning, Advance MapReduce, Hadoop Project: Advance MapReduce programming and error handling.

 

5. Module 5

Pig and Pig Latin

Learning Objectives – In this module, you will learn what is Pig, in which type of use case we can use Pig, how Pig is tightly coupled with MapReduce, and Pig Latin scripting.

Topics – Installing and Running Pig, Grunt, Pig’s Data Model, Pig Latin, Developing & Testing Pig Latin Scripts, Writing Evaluation, Filter, Load & Store Functions, Hadoop Project: Pig Scripting.

 

6. Module 6

Hive and HiveQL

Learning Objectives – This module will help you in understanding Apache Hive Installation, Loading and Querying Data in Hive and so on.

Topics – Hive Architecture and Installation, Comparison with Traditional Database, HiveQL: Data Types, Operators and Functions, Hive Tables(Managed Tables and External Tables, Partitions and Buckets, Storage Formats, Importing Data, Altering Tables, Dropping Tables), Querying Data (Sorting And Aggregating, Map Reduce Scripts, Joins & Subqueries, Views, Map and Reduce side Joins to optimize Query).

 

7. Module 7

Advance Hive, NoSQL Databases and HBase

Learning Objectives – In this module, you will understand Advance Hive concepts such as UDF. You will also acquire in-depth knowledge of what is HBase, how you can load data into HBase and query data from HBase using client.

Topics – Hive: Data manipulation with Hive, User Defined Functions, Appending Data into existing Hive Table, Custom Map/Reduce in Hive, Hadoop Project: Hive Scripting, HBase: Introduction to HBase, Client API’s and their features, Available Client, HBase Architecture, MapReduce Integration.

 8. Module 8

Advance HBase and ZooKeeper

Learning Objectives – This module will cover Advance HBase concepts. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper and how to Build Applications with Zookeeper.

Topics – HBase: Advanced Usage, Schema Design, Advance Indexing, Coprocessors, Hadoop Project: HBase tables The ZooKeeper Service: Data Model, Operations, Implementation, Consistency, Sessions, and States.

 9. Module 9

Hadoop 2.0, MRv2 and YARN

Learning Objectives – In this module, you will understand the newly added features in Hadoop 2.0, namely, YARN, MRv2, NameNode High Availability, HDFS Federation, support for Windows etc.

Topics – Schedulers:Fair and Capacity, Hadoop 2.0 New Features: NameNode High Availability, HDFS Federation, MRv2, YARN, Running MRv1 in YARN, Upgrade your existing MRv1 code to MRv2, Programming in YARN framework.

10.Module 10

Hadoop Project Environment and Apache Oozie

Learning Objectives – In this module, you will understand how multiple Hadoop ecosystem components work together in a Hadoop implementation to solve Big Data problems. We will discuss multiple data sets and specifications of the project. This module will also cover Apache Oozie Workflow Scheduler for Hadoop Jobs.

 

Who Should do this course?

Administrators, IT managers, architects, developers and anyone wanting to take advantage of big data analytics offered by Hadoop. Basic knowledge of coding is helpful but not required.

What is duration and fees for Hadoop Course?

With approximate range between 2 to 3 months, there are more options like fast track training and online training options are also available. Fees is reasonable according to market and industry standards.

What is the scope of Hadoop?

Big data analytics is integral part of IT industry. With the onset of ustructured data and its use, Hadoop being a very useful tool for big analytics.

What are the payment options available?

Different payment options are available suiting your needs. We accept credit cards, debit cards cheque cash , netbanking, money wallets.

Will I get a Job after the course? How?

With tieup through our job consultancy firm, SkyJobs, we make sure each and every candidate in placed with a top tier firm.

With guaranteed results from last 17 Years, we make you stand out in the market with hands on knowledge and better understanding of current scenarios in the market, in turn helping your to automatically get placed with MNCs.

Best Hadoop Training
  • Best Institute for Hadoop Training
  • Experienced Faculty
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