Hadoop is considered as an open- source software frame designed for storehouse and processing of large scale variety of data on clusters of commodity tackle. The Hadoop Training in Chennai offers a Hadoop software library is a frame that allows the data distributed recycling across clusters for calculating using simple programming models called Map Reduce. It’s designed to gauge up from single waiters to a cluster of machines and each immolation original calculation and storehouse inefficiently. It works in a series of chart- reduce jobs and each of these jobs is high- quiescence and depends on each other. So no job can start until the former job has been finished and successfully completed. Hadoop course in Chennai provides results typically include clusters that are hard to manage and maintain. In numerous scripts, it requires integration with other tools like a mahout,etc.Hadoop Classes in Ashok nagar, Chennai is a big platform which needs in- depth knowledge that you’ll learn from Stylish Big Data Hadoop classes in Chennai. We’ve another popular frame that works with Apache Hadoop i.e. Spark. Apache Spark allows software inventors to develop complex,multi-step data operation patterns. It also supports in- memory data participating across DAG (Directed Acyclic Graph) grounded operations, so that different jobs can work with the same participated data. Spark runs on top of the Then at Yoloshy Technologies, we’ve assiduity-standard Big Data Hadoop Classes in Chennai designed by IT professionals. The training we give is 100 practical. We give 200 assignments, POC’s and real- time systems. Also CV jotting, mock tests, interviews are taken to make the seeker assiduity-ready. Yoloshy Technologies aims to give detailed notes on Hadoop inventor training which makes it a Bravery Big Data Hadoop Classes in Chennai interview tackle and reference books to every seeker for in- depth study. The Apache Hadoop software library is a frame that allows the data distributed recycling across clusters for calculating using simple programming models called Map Reduce. It’s designed to gauge up from single waiters to a cluster of machines and each immolation original calculation and storehouse inefficiently.

Download Syllabus

Introduction to Hadoop
♦  High Availability
♦  Scaling
♦  Advantages and Challenges
Introduction to Big Data
♦  What is Big data
Big Data opportunities,Challenges
♦  Characteristics of Big data
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)
Hadoop Distributed File System (HDFS)
♦  HDFS Design & Concepts
♦  Blocks, Name nodes and Data nodes
♦  HDFS High-Availability and HDFS Federation
♦  Hadoop DFS The Command-Line Interface
♦  Basic File System Operations
♦  Anatomy of File Read,File Write
♦  Block Placement Policy and Modes
♦  More detailed explanation about Configuration files
♦  Metadata, FS image, Edit log, Secondary Name Node and Safe Mode
♦  How to add New Data Node dynamically,decommission a Data Node
dynamically (Without stopping cluster)
♦  FSCK Utility. (Block report)
♦  How to override default configuration at system level and Programming
♦  HDFS Federation
♦  ZOOKEEPER Leader Election Algorithm
♦  Exercise and small use case on HDFS
Map Reduce
♦  Map Reduce Functional Programming Basics
♦  Map and Reduce Basics
♦  How Map Reduce Works
♦  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
Optimization Techniques -> Speculative Execution, JVM Reuse and No.
♦  Types of Schedulers and Counters
♦  Comparisons between Old and New API at code and Architecture Level
♦  Getting the data from RDBMS into HDFS using Custom data types
♦  Distributed Cache and Hadoop Streaming (Python, Ruby and R)
♦  Sequential Files and Map Files
♦  Enabling Compression Codec’s
♦  Map side Join with distributed Cache
♦  Types of I/O Formats: Multiple outputs, NLINEinputformat
♦  Handling small files using CombineFileInputFormat
Map Reduce Programming – Java Programming
♦  Hands on “Word Count” in Map Reduce in standalone and Pseudo
distribution Mode
♦  Sorting files using Hadoop Configuration API discussion
♦  Emulating “grep” for searching inside a file in Hadoop
♦  DBInput Format
♦  Job Dependency API discussion
♦  Input Format API discussion,Split API discussion
♦  Custom Data type creation in Hadoop
♦  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
<strongclass="streight-line-text"> Module 8: HBase
♦  HBase Installation, Concepts
♦  HBase Data Model and Comparison between RDBMS and NOSQL
♦  Master & Region Servers
♦  HBase Operations (DDL and DML) through Shell and Programming and
HBase Architecture
♦  Catalog Tables
♦  Block Cache and sharding
♦  DATA Modeling (Sequential, Salted, Promoted and Random Keys)
♦  Java API’s and Rest Interface
♦  Client Side Buffering and Process 1 million records using Client side
♦  HBase Counters
♦  Enabling Replication and HBase RAW Scans
♦  HBase Filters
♦  Bulk Loading and Co processors (Endpoints and Observers with
♦  Real world use case consisting of HDFS,MR and HBASE
♦  Hive Installation, Introduction and Architecture
♦  Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
♦  Meta store, Hive QL
♦  OLTP vs. OLAP
♦  Working with Tables
♦  Primitive data types and complex data types
♦  Working with Partitions
♦  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
Dynamic Partition
♦  Differences between ORDER BY, DISTRIBUTE BY and SORT BY
♦  Bucketing and Sorted Bucketing with Dynamic partition
♦  RC File
♦  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
♦  Pig Installation
♦  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 and Joining
♦  Debugging commands (Illustrate and Explain)
♦  Validations,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, Load and Write JSON DATA using
♦  Piggy Bank
♦  Hands on Exercises
♦  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
♦  HCatalog Installation
♦  Introduction to HCatalog
♦  About Hcatalog with PIG,HIVE and MR
♦  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, Tail Source
♦  Log User information using Java program in to HBASE using LOG4J
and Avro Source, 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
More Ecosystems
♦  HUE.(Hortonworks and Cloudera)
♦  Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers,
Coordinators and Bundles.,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
♦  Zoo Keeper
♦  HBASE Integration with HIVE and PIG
♦  Phoenix
♦  Proof of concept (POC)
♦  Spark Overview
♦  Linking with Spark, Initializing Spark
♦  Using the Shell
♦  Resilient Distributed Datasets (RDDs)
♦  Parallelized Collections
♦  External Datasets
♦  RDD Operations
♦  Basics, Passing Functions to Spark
♦  Working with Key-Value Pairs
♦  Transformations
♦  Actions
♦  RDD Persistence
♦  Which Storage Level to Choose?
♦  Removing Data
♦  Shared Variables
♦  Broadcast Variables
♦  Accumulators
♦  Deploying to a Cluster
♦  Unit Testing
♦  Migrating from pre-1.0 Versions of Spark
♦  Where to Go from Here

♦   Real-time Experts with Industry experience.

♦   We work on your demanding skills based on corporate expectation.

  100% placement with Better Remuneration.

♦   Live project experience.

  Softskill development with Resume Modification.

1. Do you provide placement assistance?

            Yes, we are one of the trusted institute helping our participants with genuine placement assistance in good companies. We will schedule more no. of interviews for the candidates & we will give our full support until he/she got placement in a good company.


2. Will I get on job support, If I get doubts in my projects?

            Yes, our team will support you for any clarification you need in your projects.


3. Does your institute provide any weekend classes for professionals?

           There are number of institutes which are providing more no. of courses but there is hardly any institute Except Yoloshy Technologies which is providing 100% professional training with flexible timings in weekdays, weekends and online also. Yoloshy Technologies is the only institute which provides weekend classes specially for working professionals. Many of professionals who don’t get time in weekdays but want to learn these courses they can opt the courses with Yoloshy Technologies as it provide professional training with individual approach and with 100% Job assurance.


Why Students Prefer YOLOSHY TECHNOLOGIES Hadoop Course?

                                               YOLOSHY TECHNOLOGIES At Chennai offers services Hadoop for related programs. Yoloshy provide our immersive graduates with 100 percent Placement help, training, and opportunities that improve information and skills. Yoloshy offer you coaching based on current business standards. Building a strong portfolio with a highly built resume for Hadoop. YOLOSHY TECHNOLOGIES academy the leading Hadoop Training Institute in Chennai offers classroom sessions which are fully based on practical session. Yoloshy have created multiple batches with restricted students so the tutor will keep a watch on individual attention to each student. YOLOSHY TECHNOLOGIES sessions are more interactive and well-structured seeking the convenience of our students. Our experts of YOLOSHY TECHNOLOGIES are high-level graduates who work in top MNC’s. YOLOSHY TECHNOLOGIES Hadoop trainers are dynamic and have good command in communication which helps to deliver the best methodologies in our Digital Marketing  in Chennai.


YOLOSHY TECHNOLOGIES  is a live platform that makes new-age software, technology and creative skills with varying Personal and Professional Goals.

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