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    About Hadoop Training

    Hadoop, Hadoop Cluster, how to store Big Data using Hadoop (HDFS) and how to process / analyze the Big Data using Map-Reduce.

    Hadoop Training Course Prerequisites

    Basic Unix Commands
    Core Java (OOPS Concepts, Collections, Exceptions) – For Map-Reduce Programming
    SQL Query Knowledge – For Hive Queries
    Hardware and Software 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)
    Hadoop Training Course Duration

    50 Hours, daily 1:30 Hours

    Hadoop Course Content

    Introduction to Hadoop

    High Availability
    Scaling
    Advantages and Challenges

    Introduction to Big Data

    What is Big data
    Big Data opportunities
    Big Data 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).

    The 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
    Anatomy of 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.
    How to 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.

    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 APIs 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 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
    Input Split API discussion
    Custom Data type creation in Hadoop.

    NOSQL

    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

    HBase Installation
    HBase 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 Coprocessors (Endpoints and Observers with programs).
    Real world use case 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 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 & Joining
    Debugging commands (Illustrate and Explain).
    Validations in PIG.
    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.
    How to 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 the Java program in to HDFS using LOG4J and Avro Source
    Log User information using Java program in to HDFS using Tail Source
    Log User information using the Java program in to HBASE using LOG4J and Avro Source
    Log User information using Java program in to HBASE using 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).

    Oozie

    Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles.
    Workflow to show how to schedule Sqoop Job, Hive, MR and PIG.
    Real world Use case that 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
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