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About Hadoop

Hadoop Development course teaches the skill set required for the learners how to setup Hadoop Cluster, how to store Big Data using Hadoop (HDFS) and how to process/analyze the Big Data using Map-Reduce Programming or by using other Hadoop ecosystems. Attend Hadoop Training demo by Real-Time Expert.

Hadoop Course System Requirements

• Any Linux flavor OS (Ex: Ubuntu/Cent OS/Fedora/RedHat Linux) with 4 GB RAM (minimum), 100 GB HDD
• Java 1.6+
• Open-SSH server & client
• MYSQL Database
• Eclipse IDE
• VMWare (To use Linux OS along with Windows OS)

Who can Pursue Artificial Intelligence?

  • Any Degree pursuing or Graduated,
  • Bachelor’s, Master’s, PhD, and
  • Anyone interested in course
Enroll to Course Now
ServicesEssential Course PackageMid-Level Career BoosterMastery Career Package
Duration3 Months7 Months7 Months + 1 Year
Course Completion Certificate
One to One
Resume Preparation
Mock Interviews
Job Assistance
Job Guarantee


Introduction to Hadoop

• High Availability • Scaling • Advantages and Challenges

ntroduction 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 level • HDFS Federation • ZOOKEEPER Leader Election Algorithm • Exercise and small use case on HDFS

Web Scraping

  • Url
  • Beautiful Soup

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. Slots • 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) • YARN • 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


• 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 • SPLITS • 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 Buffering • HBase Counters • Enabling Replication and HBase RAW Scans • HBase Filters • Bulk Loading and Co processors (Endpoints and Observers with programs) • 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 • INDEXES and VIEWS • MAPSIDE JOINS • 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 PIG • 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

Job Roles

• Hadoop Engineer

Components of Hadoop?

o Hadoop HDFS - Hadoop Distributed File System (HDFS) is the storage unit of Hadoop. o Hadoop MapReduce - Hadoop MapReduce is the processing unit of Hadoop. o Hadoop YARN - Hadoop YARN is a resource management unit of Hadoop.

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Best Artificial Intelligence Training in Hyderabad with 100% placement Assistance. Learn Data Science with Python, Data Analysis, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Statistics and Tableau.


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