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Big Data and Hadoop

BigData is the latest buzzword in the IT Industry. Apache’s Hadoop is a leading Big Data platform used by IT giants Yahoo, Facebook & Google. This course is geared to make a Hadoop Expert.

A key aspect of the resiliency of hadoop clusters comes from the software ability to deduct and handle failures at the application layer. Hadoop has two main subprojects: first MapReduce, the framework that understand and assigns work to the nodes in a cluster and secondly, HDFS, a distributed file system that spans all the nodes in a Hadoop cluster for data storage.
Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of servers. It is designed to scale up from a single server to thousands of machine, with a very high degree of fault tolerance. Hadoop was derived from Google’s MapReduce and the Google file system. Yahoo! was the originator and has beena major contributor and uses Hadoop across its business. Other major users include Facebook, IBM, Twitter, American Airlines, LinkedIn, The New York Times and many more.

* Note:The Number of Project will be Covered According to the Duration of that particular Program.
All training modules are based on REAL TIME LIVE PROJECTS.

Fees:
Course Duration (7 Days)- Rs. 3,500/- per participant including of all taxes.
Course Duration (15 Days)- Rs. 5,000/- per participant including of all taxes.
Course Duration (30 Days)- Rs. 7,500/- per participant including of all taxes.


DAY-1

Module 1. What is Big Data & Why Hadoop?

 What is Big Data?

 Traditional data management systems and their limitations

 What is Hadoop?

 Why is Hadoop used?

 The Hadoop eco-system

 Big data/Hadoop use cases

Module 2. HDFS (Hadoop Distributed File System) and installing Hadoop on single node

 HDFS Architecture

 HDFS internals and use cases

 HDFS Daemons

 Files and blocks

 Namenode memory concerns

 Secondary namenode

 HDFS access options

 Installing and configuring Hadoop

 Hadoop daemons

 Basic Hadoop commands

 Hands-on exercise

DAY-2

Module 3. Advanced HDFS concepts

 HDFS workshop

 HDFS API

 How to use configuration class

 Using HDFS in MapReduce and programmatically

 HDFS permission and security

 Additional HDFS tasks

 HDFS web-interface

 Hands-on exercise

DAY-3

Module 4. Cloud computing overview and installing Hadoop on multiple nodes

 Cloud computing overview

 SaaS/PaaS/IaaS

 Characteristics of cloud computingSaaS/PaaS/IaaS

 Cluster configurationsSaaS/PaaS/IaaS

 Configuring Masters and Slaves

 Module 5.Introduction to MapReduce

 MapReduce basics

 Functional programming concepts

 List processing

 Mapping and reducing lists

 Putting them together in MapReduce

 Word Count example application

 Understanding the driver, mapper and reducer

 Closer look at MapReduce data flow

 Additional MapReduce functionality

 Fault tolerance

 Hands-on exercises

Module 6. MapReduce workshop

 Hands-on work on MapReduce

DAY-4

Module 7. Advanced MapReduce concepts

 Understand combiners & partitioners

 Understand input and output formats

 Distributed cache

 Understanding counters

 Chaining, listing and killing jobs

 Hands-On Exercise

Module 8. Using Pig and Hive for data analysis

 Pig program structure and execution process

 Joins & filtering using Pig

 Group & co-group

 Schema merging and redefining functions

 Pig functions

 Understanding Hive

 Using Hive command line interface

 Data types and file formats

 Basic DDL operations

 Schema design

 Hands-on examples

DAY-5

Module 9. Introduction to HBase, Zookeeper & Sqoop

 HBase overview, architecture & installation

 HBase admin: test

 HBase data access

 Overview of Zookeeper

 Sqoop overview and installation

 Importing and exporting data in Sqoop

 Hands-on exercise

DAY-6

Module 10. Introduction to Oozie, Flume and advanced Hadoop concepts

 Overview of Oozie and Flume

 Oozie features and challenges

 How does Flume work

 Connecting Flume with HDFS

 YARN

 HDFS Federation

 Authentication and high availability in Hadoop

Day -7

 Introduction: What is Data Science?, Getting started with R, Exploratory Data Analysis, Review of probability and probability distributions, Bayes Rule

 Supervised Learning,  Regression, polynomial regression, local regression, k-nearest neighbors,

 Unsupervised Learning,  Kernel density estimation, k-means, Naive Bayes, Data and Data Scraping

 Classification, ranking, logistic regression

 Ethics, time series, advanced regression

DAY 1 and DAY 2

1. Understanding Big Data and Hadoop

Learning Objectives -  In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, Rack Awareness.

Topics -  Big Data, Limitations and Solutions of existing Data Analytics Architecture, Hadoop, Hadoop Features, Hadoop Ecosystem, Hadoop 2.x core components, Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework, Anatomy of File Write and Read, Rack Awareness.

DAY 3 and DAY 4

2. Hadoop Architecture and HDFS

Learning Objectives - In this module, you will learn the Hadoop Cluster Architecture, Important Configuration files in a Hadoop Cluster, Data Loading Techniques.

Topics - Hadoop 2.x Cluster Architecture - Federation and High Availability, A Typical Production Hadoop Cluster, Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Password-Less SSH, MapReduce Job Execution, Data Loading Techniques: Hadoop Copy Commands, FLUME, SQOOP.

DAY 5 and DAY 6

3. Hadoop MapReduce Framework - I

Learning Objectives - In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will learn about YARN concepts in MapReduce.

Topics - MapReduce Use Cases, Traditional way Vs MapReduce way, Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components, YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program, Demo on MapReduce.

DAY 7

4. Hadoop MapReduce Framework - II

Learning Objectives - In this module, you will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets.

Topics - Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce Job Submission Flow, Demo of Input Splits, MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set.

DAY 8

5. Advance MapReduce

Learning Objectives - In this module, you will learn Advance MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and how to deal with complex MapReduce programs.

Topics - Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format.

DAY 9

6. Pig

Learning Objectives - In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting.

Topics - About Pig, MapReduce Vs Pig, Pig Use Cases, Programming Structure in Pig, Pig Running Modes, Pig components, Pig Execution, Pig Latin Program, Data Models in Pig, Pig Data Types.

Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF, Pig Demo on Healthcare Data set.

DAY 10 and DAY 11

7. Hive

Learning Objectives - This module will help you in understanding Hive concepts, Loading and Querying Data in Hive and Hive UDF. 

Topics - Hive Background, Hive Use Case, About Hive, Hive Vs Pig, Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database, Hive Data Types and Data Models, Partitions and Buckets, Hive Tables(Managed Tables and External Tables), Importing Data, Querying Data, Managing Outputs, Hive Script, Hive UDF, Hive Demo on Healthcare Data set.

DAY 12 and DAY 13

8. Advance Hive and HBase

Learning Objectives - In this module, you will understand Advance Hive concepts such as UDF, dynamic Partitioning. You will also acquire in-depth knowledge of HBase, Hbase Architecture and its components.

Topics - Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive : Thrift Server, User Defined Functions.

HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, HBase Cluster Deployment.

DAY 14

9. Advance HBase

Learning Objectives - This module will cover Advance HBase concepts. We will see demos on Bulk Loading , Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper.

Topics - HBase Data Model, HBase Shell, HBase Client API, Data Loading Techniques, ZooKeeper Data Model, Zookeeper Service, Zookeeper, Demos on Bulk Loading, Getting and Inserting Data, Filters in HBase.

DAY-15

10. Oozie and Hadoop Project

Learning Objectives - In this module, you will understand working of multiple Hadoop ecosystem components together in a Hadoop implementation to solve Big Data problems. We will discuss multiple data sets and specifications of the project. This module will also cover Flume & Sqoop demo and Apache Oozie Workflow Scheduler for Hadoop Jobs.

Topics - Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web Console, Hadoop Project Demo.

Introduction to Big Data
 What is Big data
 Big Data opportunities
 Big Data Challenges
 Characteristics of Big data 

Introduction to Hadoop

 What is Hadoop
 Relationship between hadoop and Bigdata
 Advantages and Challenges
 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 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, NLINE input format.
 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 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 & 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.

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
 Log User information using Java program in to HDFS using Tail Source
 Log User information using 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 

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