Bigdata hadoop training in bangalore

BIGDATAHADOOPTRAINING INBANGALORE MOB : 9513332301 / 02 / 03 WEB : WWW.TIBACADEMY.IN

BIGDATA HADOOP TRAINING IN BANGALORE • TIB Academy is one of the Best Institutes of Hadoop online Training and classroom Training in Bangalore that offers high quality training with affordable training course fees. Hadoop is one of the best solution to big data problem. It is a trending and newly emerging open source technology which is used for data processing and data analytics using HDFS (Hadoop Distributed File System) and Mapreduce algorithms. Big Data Analytics primarily involves in data collection and conversion for collecting massive unstructured data from different sources and converting those raw data into a preferable format.

• Bigdata-Hadoop is an innovative technology which is used to handle massive data and to gain clear understanding on each and every customers and competitors in the real world scenario. One of the major demand of each and every company is “Data Security”. Using Hadoop, that can be successfully achieved via preventing the data malpractices from the hackers. With this Hadoop course, enable yourself with key concepts of Big Data, Hadoop installation, Mapreduce algorithms, MongoDB, HDFS, Flume, zookeeper and sqoop.

• TIB Academy is the best Hadoop training institute in Marathahalli, where you can gain guidance from our industrial experts with extraordinary subject proficiency and teaching skill. We are providing the best training and placement support with affordable Hadoop training course cost in Bangalore. We are currently providing both Hadoop development and Hadoop administration training in Bangalore. Our tutors can guide you to clear the examinations on Hadoop certification in Bangalore and our training program starts from the fundamentals of database management until the advanced level of analytic applications.

HADOOP SYLLABUS • Basic / introduction • Multi-Threading • OpenMP (Open Multiprocessing) and MPI (Message Passing Interface) • Performance tuning and optimization:- Matrix Multiplication, Uniqueword count problem, Distributed computing concepts, Designing and caveats, Distributed Debugging, Network challenges.

HADOOP OVERVIEW • Why Hadoop?

• Data Storage and Analysis • Hadoop v/s RDBMS • Brief history of Hadoop • Architecture of Hadoop • Overview of HDFS (Hadoop Distributed File System) and MR (Map Reduce) framework • Overview of problems solved by Hadoop • Data Mining • Web Mining • Natural Language Processing • K-means clustering • Sentimental Analysis

SETTING UP HADOOP • Pseudo Mode • Cluster • Common Errors when running Hadoop cluster • Incompatible name space IDs • Protocol version mismatch • Safe mode exception

HDFS – HADOOP DISTRIBUTED FILE SYSTEM • HDFS Design and Architecture • HDFS Concepts • Interacting HDFS using command line

• Interacting HDFS using JAVA APIs • Running MR application on Local File system • pseudo mode • cluster mode • Creating and deleting directories on HDFS • Creating and deleting files on HDFS • Reading and writing files on HDFS • Moving files with in HDFS • Renaming files • Submitting Jobs • Data flow while reading and writing

MAP REDUCE PROGRAMMING MODEL •

Developing Map Reduce Application

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Configuring environment

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Writing Junit test cases for Mapper

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Reducer and Driver

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Using local runner

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Running on pseudo mode

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Running on cluster mode

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Launching the job

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Using Command Line

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Using Java API

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Tuning the job

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Using Combiner

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Using Compression

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Using HProf (Profiler)

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Phases in Map Reduce Framework

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Partitioning

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Shuffling and Sorting

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How Hadoop runs a Map Reduce Job

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Job Submission

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Job Initialization

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Task Assignment

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Task Execution

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Map Reduce Input and Output Formats

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Text IO, Sequence IO

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KeyValue IO

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DataBase IO

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Advance Concepts

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Chain Mapper and Chain Reducer

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Using Counters

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Distributed Cache

HADOOP PROGRAMMING LANGUAGE • Hive

• Installation • Creating tables • Writing Hive Queries • Pig • Installation • Concepts • Data processing operators • Writing UDFs

NO SQL DATABASES - MONGODB

• Concepts – SQL v/s NoSQL

• Case Studies / Projects • Flume/zookeeper/sqoop

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