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Best Hadoop Classes and Hadoop Online Training

Big Data and Hadoop

Why Big-Data and Hadoop

A recent IDC study, sponsored by EMC2, predicts that the “digital universe” , the data that is generated in digital form by humankind, will double every two years and will reach 40,000 exabytes by 2020. The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. Hadoop online Training, which is not less than a panacea for all those companies working with BIG DATA in a variety of applications has become an integral part for storing, handling, evaluating and retrieving hundreds or even petabytes of data. The main source of this huge data is complex and unstructured data which is coming from the follows sources:

Today, every other industry is generating Big Data and they need Hadoop experts to come up with the BIG Insights as a value addition/New offering to the existing customer and to get new customers.More interestingly, all existing big players which are into Analytics, even they are connecting to Hadoop to get massaged/processed data. There is a potential dearth of good software engineers/programmers on Big data. Considering the above fact, we are in an industry to cater to these services so that more and more people are committed to the cause

Best Hadoop Classes

Duration

09 Sessions, each session of 03-04 hours

Why Petaa-Bytes ?

  • 100% Placement assistance.
  • Petaa-Bytes believes in making you Hadoop expert than merely understanding basic concepts
  • Prepare you for Interview and CLOUDERA certification
  • Post Course, 24*7 support for any queries pertaining to Hadoop
  • Real life project covering all the components covered during the course
  • Faculty is having around 15+ yrs of IT experience. CLOUDERA and Oracle certified

What is Big Data ?

Big data is basically vast amount of data which cannot be effectively processed, captured and analyzed by traditional database. Though the “big” in Big Data is subjective, McKinsey estimates that it would anywhere between few dozen terabytes to petabytes for most of the sectors.

Big Data information explosion is mainly due to the vast amounts of data generated by social media platform, data input from Omni-channels, various mobile devices, user generated data, multi-media data and so on. Analysts term this as an expanding “Digital Universe”.



What is Apache Hadoop ?

Hadoop Online Training project develops open-source software for reliable, scalable, distributed computing.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets 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. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.



Course Content

Best Hadoop Classes and Hadoop Online Training is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Hadoop Cluster, Advanced Map-Reduce, Oozie, Hbase Zookeeper etc. will be covered in the course.


Project Work:- Towards the end of the course, you will be working on a live project which will be a large dataset and you will use PIG, HIVE, HBase and MapReduce to do the analytics. The final project is a reallife business case on some open data set.


Course Duration:- 09 Sessions, each of 3-4 Hrs + Hands on session(for classroom) + Assignments + Project Work



MODULE 1: Hadoop and its Architecture

  • 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
Hadoop Online Training

MODULE 2: Single/Multi Node Cluster Configuration

  • 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

MODULE 3: Hadoop MapReduce framework

  • Hadoop Data Types
  • Hadoop MapReduce paradigm
  • Map and Reduce tasks
  • Map Reduce 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

MODULE 4: Advance MapReduce

  • Counters
  • Custom Writables
  • Unit Testing: JUnit and MRUnit testing framework
  • Error Handling
  • Tuning
  • Advance MapReduce
  • Hadoop Project: Advance MapReduce programming and error handling.
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MODULE 5: Pig Latin

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

MODULE 6: Hive (HQL - Hive Query Language)

  • 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)

MODULE 7: Advance Hive, Hadoop’s NOSql Database : HBase

  • 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

MODULE 8: Advance HBase and ZooKeeper

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

  • Schedulers :Fair and Capacity
  • Hadoop 2.0 New Features: NameNode High Availability
  • HDFS Federation
  • MRv2
  • YARN and Running MRv1 in YARN

Take Away From The Course