Big Data and Hadoop Adminstrator Certification Course

  • About Course
  • Course Overview
  • Key Features
  • Agenda
  • FAQ
  • Training Calendar

Agenda

Module 1: Introduction to Big Data and Hadoop

    • What is Big Data?
    • The Rise of Bytes
    • Data Explosion and its Sources
    • Types of Data – Structured, Semi-structured, Unstructured data
    • Why did Big Data suddenly become so prominent
    • Data – The most valuable resource
    • Characteristics of Big Data – IBM’s Definition
    • Limitations of Traditional Large-Scale Systems
    • Various Use Cases for Big Data
    • Challenges of Big Data
    • Hadoop Introduction - What is Hadoop? Why Hadoop?
    • Is Hadoop a fad or here to stay? - Hadoop Job Trends
    • History and Milestones of Hadoop
    • Hadoop Core Components – MapReduce & HDFS
    • Why HDFS?
    • Comparing SQL Database with Hadoop
    • Understanding the big picture - Hadoop Eco-Systems
    • Commercial Distribution of Hadoop – Cloudera, Hortonworks, MapR, IBM BigInsight, Cloud Computing - Amazon Web Services, Microsoft Azure HDInsight
    • Supported Operating Systems
    • Organizations using Hadoop
    • Hands on with Linux File System
    • Hadoop Documentation and Resources

Module 2: Getting Started with Hadoop Setup

    • Deployment Modes – Standalone, Pseudo-Distributed Single node, Multinode
    • Demo Pseudo-Distributed Virtual Machine Setup on Windows
      VMware Player - Introduction
      Install VMware Player
      Open a VM in VMware Player
    • Hadoop Configuration overview
      Configuration parameters and values
      HDFS parameters
      MapReduce parameters
      YARN parameters
      Hadoop environment setup
      Environment variables
    • Hadoop Security - Authentication/Authorization
    • Hadoop Core Services – Daemon Process Status using JPS
    • Overview of Hadoop WebUI
      Firefox Bookmarks
      Web Ports
    • Eclipse development environment setup
    • References

Module 3: Hadoop Architecture and HDFS

    • Introduction to Hadoop Distributed File System
    • Regular File System v/s HDFS
    • HDFS Architecture
    • Components of HDFS - NameNode, DataNode, Secondary NameNode
    • HDFS Features - Fault Tolerance, Horizontal Scaling
    • Data Replication, Rack Awareness
    • Setting up HDFS Block Size
    • HDFS2.0 - High Availability, Federation
    • Hands on with Hadoop HDFS,WebUI and Linux Terminal Commands
    • HDFS File System Operations
    • Name Node Metadata, File System Namespace, NameNode Operation,
    • Data Block Split, Benefits of Data Block Approach, HDFS - Block Replication Architecture, Block placement, Replication Method, Data Replication Topology, Network Topology, Data Replication Representation
    • Anatomy of Read and Write data on HDFS
    • Failure and Recovery in Read/Write Operation
    • Hadoop Component failures and recoveries
    • HDFS Programming Basics – Java API
      Java API Introduction
      Hadoop Configuration API
      HDFS API Overview
      HDFS File CRUD API
      HDFS Directory CRUD API
      Accessing HDFS Programmatically
    • HDFS Programming Advanced – Hadoop I/O
      File Compression, Decompression
      Writable Class Hierarchy
      Data Type Serialization, Deserialization
      Sequence Files
    • Running inbuilt Map Reduce Examples
      Run MapReduce example to get a high level understanding
      Checking the output of M/R Job – console, WebUI
      Understanding the dump of M/R Job
    • When Hadoop is not suitable
    • Reference

Module 4: Pseduo Distributed Cluster Installation

    • HDFS Architecture
    • Data Flow (File Read , File Write)
    • HDFS Shell Commands
    • Hadoop Archives
    • Configuration - Installation of pseudo Distributed Cluster - Configuration Files Introduction to MapReduce - Compression (LZO, Snappy) – Installation of Snappy

Module 5: MultiNode Cluster Installation

    • Multi Node Cluster Setup using AWS Cloud Machines
    • Cluster Hardware Considerations – Operating Systems
    • Commands (fsck, job, dfsadmin) –
    • Job Schedulers (Fair Scheduler , Capacity Scheduler)
    • RackAwareness Policy
    • Balancing -
    • NameNode Failure and Recovery - commissioning and Decommissioning a Node

Module 6: Data Warehousing - Pig

    Pig Data Flow Language – MapReduce using Scripting

    • Challenges Of MapReduce Development Using Java
    • Need for High Level Languages - Pig
    • PIG vs MapReduce
    • What is/n’t PIG, PigLatin, Grunt Shell
    • Where to/not to use Pig?
    • Pig Installation and Configuration
    • Architecture: The Big Picture, Pig Components
    • Execution Environments - Local, Mapreduce
    • Different ways of Invoking Pig – Interactive, Batch
    • Pig Example: Data Analysis in Pig Latin
    • Data Flow in Hadoop
    • Quickstart and Interoperability
    • Data Model and Nested Data Model
    • Expression in Pig Latin
    • Pig Data Types,
    • Nulls in Pig Latin
    • Pig Macros
    • Pig Operation
    • Core Relational Operators – Load, Store, Filter, Transform, Join, Group, CoGroup, Union, Foreach, Sort/Order, Combine/Split, Distinct, Filter, Limit, Describe, Explain, Illustrate
    • Group v/s CoGroup v/s Join
    • PIG Latin: File Loaders & in built UDF(Python, Java) usage
    • PIG v/s SQL
    • Implementation & Usage of Pig UDF
    • Hands on with Pig – 3 different datasets
    • Reference


Module 7 : Data Warehousing - Hive and HiveQL

    • Limitations of MapReduce
    • Need for High Level Languages
    • Analytical OLAP - Datawarehousing with Apache Hive and Apache Pig HiveQL- SQL like interface for MapReduce
    • What is Hive, Background, Hive QL
    • Where to use Hive? Why use Hive when Pig is here?
    • Pig v/s Hive
    • Hive Installation, Configuration Files
    • Hive Components, Architecture and Metastore
    • Metastore – configuration
    • Driver, Query Compiler, Optimizer and Execution Engine
    • Hive Server and Client components
    • Hive Data Types
    • Hive Data ModeFile Formats
    • Hive Example
    • Hive DDL
      CREATE,ALTER,DROP,TRUNCATE
      Create/Show Database
      Create/Show/Drop Tables
    • Hive DML
      SELECT, INSERT, OVERWRITE, EXPLAIN
      Load Files & Insert Data into Tables
    • Managed Tables v/s External Tables – Loading Data
    • Hive QL - Select, Filter, Join, Group By, Having, Cubes-Fact/Dimension(Star Schema)
    • Implementation & Usage of Hive UDF, UDAF, UDTF and SerDe
    • Partitioned Table - loading data
    • Clustered Table – loading data in Clustered Table Views
    • Bucketing
    • Multi-Table Inserts
    • Using HCatalog
    • Joins
    • Hands on with Hive – CRUD - Get,Put,Delete,Scan
    • Limitations of Hive
    • SQLv/s Hive
    • Reference

Module 8: NoSQL Databases - HBase

    • NoSQL Introduction
    • RDBMS (SQL) v/s HBase (NoSQL)
    • Transactional (OLTP)
    • RDBMS – Benefits, ACID, Demarits
    • CAP Theorem and Eventual consistency
    • Row Oriented v/s Column Oriented Storage
    • NoSQL: ColumnDB(HBase,Cassandra),Document(MongoDB,CouchDB, MarkLogic),GraphDB(Neo4J), KeyValue(Memcached, Riak, Redis, DynamoDB)
    • What is HBase?
    • Synopsis of how typical RDBMS scaling story runs
    • HBase comes as a rescue
    • HBase Introduction, Installation, Configuration
    • HBase Overview: part of Hadoop Ecosystem
    • Problems with Batch Processing like MR
    • HBase v/s HDFS
    • Batch vs. Real Time Data Processing
    • Use-cases for Real Time Data Read/Write
    • Seek v/s Transfer
    • HBase Storage Architecture
    • Write Path, Read Path
    • HBase components - HMaster, HRegionServer
    • ZooKeeper
    • Replication
    • HBase Data Model
    • HBase Schema Design: Tall-Narrow Tables and Flat-Wide Tables
    • Column Families
    • Column Value & Key Pair
    • HBase Operation - Memstore / HFile / WAL
    • HBase Client - HBase Shell
    • Admin Operation
    • CRUD Operations
      Create via Put method
      Read via Get method
      Update via Put method
    • Delete via Delete method
    • Creating table, table properties, versioning, compression
    • Bulk Loading HBase
    • Hive, Pig - HBase Integration
    • Accessing HBase using Java Client v/s Admin API
      Introduction to Java API
      Read / Write path
      CRUD Operations – Create, Read, Update, Delete
      Drop
      Scans
      Scan Caching
      Batch Caching
      MapReduce Integration
      Filters
      Counters
      Co-processors
      Secondary Index
    • Compaction – major, minor
    • Splits
    • Bloom Filters
    • Caches
    • Performance Tuning
    • Apache Phoenix
    • When Would I Use Apache HBase?
    • Companies Using HBase
    • Hands on HBase, Cassandra
    • When/Why to use HBase/Cassandra/MongoDB/Neo4J?
    • Who is using What?
    • Reference

Module 9: Import/Export Data - Sqoop, Flume

    • Setup MySQL RDBMS
    • Introduction to Sqoop
    • Installing Sqoop, Configuration
    • Why Sqoop
    • Benefits of Sqoop
    • Sqoop Processing
    • How Sqoop works
    • Sqoop Architecture
    • Importing Data – to HDFS, Hive, HBase
    • Exporting Data – to MySQL
    • Sqoop Connectors
    • Sqoop Commands
    • Why Flume
    • Flume - Introduction
    • Flume Model
    • Scalability In Flume
    • How Flume works
    • Flume Complex Flow - Multiplexing
    • Hands on with Sqoop, Flume
    • Reference

Module 10: Workflows using Oozie

    MapReduce Workflows

    • Workflows Introduction
    • Decomposing Problems into MapReduce Workflow
    • Using JobControl class
    • Introduction to Oozie
    • Oozie Installation
    • Creating Oozie Workflows
    • Oozie Service/Scheduler
    • Deploy and Run Oozie Workflow
    • Oozie use-cases
    • Hands on with Oozie
    • Reference


Module 11: Administering Hadoop

    • Hadoop Deployment Modes
    • Pseudo-Distributed Mode - Virtual Machine for Hadoop Training
      VMware Player - Introduction
      Install VMware Player
      Create a VM in VMware Player
      Open a VM in VMware Player
      Oracle VirtualBox to Open a VM
      Open a VM using Oracle
    • Hadoop Cluster Configuration overview
      Configuration parameters and values
      HDFS parameters
      MapReduce parameters
      Hadoop environment setup
      ‘Include’ and ‘Exclude’ configuration files
      Site v/s Default conf files
      Environment Variables
      Scripts
      Hadoop Multi-node Installation using VM on single machine
      Hadoop Multi-Node Fully Distributed Mode Installation
      Passwordless SSH setup
      Configuration Files of Hadoop Cluster
      hadoop default.xml
      Safe Mode
      DfsAdmin
      Load Balancer
      Hadoop Ports
      Hadoop Quotas
      Security - Kerberos
    • ZooKeeper
      What is Zookeeper
      Introduction to ZooKeeper
      Who is using it
      Features of ZooKeeper
      Challenges Faced in Distributed Applications
      ZooKeeper Coordination, Architecture,
      Uses of ZooKeeper
      Entities, Data Model, Services
      ZNokde Types
      Sequential ZNodes
      Client API Functions
      Zookeeper Installation, Configuration and Running Zookeeper
      Zookeeper use cases – HBase, Hadoop, Kafka
    • Hue, Cloudera Manager, Ambari, Mesos
    • Performance Monitoring and Tuning
    • Hadoop Cluster Performance Management
      Important Hadoop tuning parameters
      Hadoop Cluster Benchmarking Jobs – How to run the jobs
      TestDFSIO, NNBench, MRBench
      Counters
      HDFS Benchmarking
    • Debugging, Troubleshooting
    • Reference

Module 12: Apache Spark

    • Spark Concepts, Installation and Architecture
    • Spark ecosystem (core, spark sql, mlib, streaming)
    • Spark Modes
    • Spark web UI
    • Spark shell
    • RDD’s In Depth
    • Partitions
    • RDD Operations / transformations
    • RDD types
    • Key-Value pair RDDs
    • MapReduce on RDD
    • Caching and persistence
    • Submitting the first program to Spark
    • Broadcast variables
    • Accumulators
    • Memory management - Executers
    • Spark SQL
    • Spark Streaming – with NetCat, Kafka
    • Reference

FAQ

Who are the Instructors?

    We believe in quality & follow a rigorous process in selecting our trainers. All our trainers are industry experts/ professionals with an experience in delivering trainings

Whom do I contact if I have further clarification?

    You can call us on +1-888-828-1956 / +91-9108460933 or drop a mail to email@it-skillstraining.com

What if I miss the online class?

    You will get the recording of the session and also you can attend the session from the next batch. you can contact help for next batch details.

What are the pre-requisites for the course?

    Knowledge in any programming languages like PHP, Python, PERL, Java, C or C++ and some familiarity with database & SQL will be an added advantage.

Is this live training, or will I watch pre-recorded videos?

    Yes demo is available

What is Hadoop Administration?
When things operate in a group, we need a manager. In computer terms, the manager is called the administrator. This administrator or admin is accountable for the maintenance of the systems in the cluster. The administrator is liable for the performance and accessibility of the systems on the cluster. Apart from this, the data present in the system and the jobs that run in it are also the administrator's duty. He/she will be required to take on tasks like configuration, monitoring, backing up, troubleshooting, upgrades, deployment, job management etc.

To start with fundamentals of Hadoop Administration one will be introduced to Hadoop framework, as basic outline about the tools and functionalities, its usages, history etc. All sorts of queries relating to why Hadoop is needed, what are its profits or advantages over the previous framework will be cleared to construct a strong foundation for the course. It will be compared with all available traditional file systems. 

Online Big Data Hadoop Administrator Certification Training Course is abundant and aspirants can take up this course to become professionals in this area. The role of a Hadoop Admin is mainly allied with tasks that involve installing and monitoring Hadoop clusters. Hadoop Admin job responsibilities might include some routine tasks, but each one in important for the efficient and continued operation of Hadoop clusters, to avert problems and to enhance the overall performance. A Hadoop admin is the person accountable for keeping the firm’s Hadoop clusters safe and running efficiently. Online Hadoop Administration Course is available for working professionals who want to make it big in this field.

Hadoop Admin Job Roles and Responsibilities
Managing big data and Hadoop clusters offers various challenges to Hadoop admins with running test data through a couple of machines. Many times, formal deployments of Hadoop fail as the administrators try to replicate the processes and procedures tested on 1 or 2 different machines across more complex Hadoop clusters.

The distinctive responsibilities of a Hadoop admin include – deploying a Hadoop cluster, upholding a Hadoop cluster, adding and removing nodes using cluster monitoring tools like Ganglia Nagios or Cloudera Manager, configuring the NameNode high availability and possession of a track of all the running Hadoop jobs.

A Hadoop administrator will have to work thoroughly with the database team, network team, BI team and application teams to make sure that all the big data applications are highly available and performing as expected. If working with open source Apache Distribution then Hadoop admins have to manually setup all the configurations - Core-Site, HDFS-Site, YARN-Site and MapRed-Site. However, when working with popular Hadoop distribution like Hortonworks, Cloudera or MapR the configuration files are setup on start-up and the Hadoop admin need not configure them manually. Hadoop admin is in control for capacity planning and estimating the requirements for lowering or increasing the capacity of the Hadoop cluster.

Course Overview

The Big Data Hadoop Administration Certification Training is designed for administrators who wish to build, manage and troubleshoot the Hadoop cluster. The course offers insights with installation, configuration, troubleshooting and other maintenance of Hadoop cluster.

Objectives of the Big Data Hadoop Administration Certification Training Course

After completion of the Online Big Data Hadoop Administration Course from IT Skills, you will be able to:  

* Acquire in-depth understanding of Hadoop administration eco-system components

* Get hands-on experience on Hadoop concepts, framework & implementation methodologies Work on real-time project using Hadoop

* Get a complete understanding of HDFS, Hadoop administration, Hadoop cluster and apache Hadoop

* Get a clear understanding of Oozie, Hive and HBase Administration

* Become skilled in HDFS and Sqoop with the help of Demos and hands-on Lab exercises

* Become expert in configuration and management of security for Hadoop clusters using Kerbero

* Monitor and Troubleshoot common Hadoop cluster issues

Who can attend?
* Data Management professional
* Architects and developers
* Data Analysts
* BI Analysts
* Consultants involved with Data
* Data scientists
* Anyone looking for a career in Big Data

Key Features

  • 32 hours of high quality training
  • Trainers are Industry experts & working professionals
  • Comprehensive up-to date contents
  • Exercises & Hands-on assignments
  • 100% Money back guarantee
  • Course completion certificate

How are the classes conducted?

  • Class Room Training
  • Instructor-Led online Training
  • Online Self learning

Money back Guarantee

  • If you don't like the training, inform us after 1st session. 100% money will be refunded with no questions asked

Group Discount

  • 10% discount for 3 or more registration

 

 

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