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Professional training on Hadoop & Big Data

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Pimple Saudagar, Pune

Course ID: 14091

Pimple Saudagar, Pune

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About 3RI Technologies Pvt Ltd

We have almost 25 trainers with us for all different technologies, we have associated with so many people in the IT industry who actually works on the real time projects have may years of expertise experience, they share their knowledge as a KT sessions or visiting faculty.
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About the Course

This course is specially designed for the people who are willing to join the Hadoop, in this we will cover all the Industrial aspects while teaching to the students.It covers all the new features of the advanced hadoop
The Trainer is a well known name in corporate world, he has more than years of experience in real time experience.

Topics Covered

1. Java
Overview of Java
Classes and Objects
Garbage Collection and Modifiers
Inheritance, Aggregation, Polymorphism
Command line argument
Abstract class and Interfaces
String Handling
Exception Handling, Multithreading
Serialization and Advanced Topics
Collection Framework, GUI, JDBC
2. Linux
Unix History & Over View
Command line file-system browsing
Bash/CORN Shell
Users Groups and Permissions
VI Editor
Introduction to Process
Basic Networking
Shell Scripting live scenarios
3. SQL
Introduction to SQL, Data Definition Language (DDL)
Data Manipulation Language(DML)
Operator and Sub Query
Various Clauses, SQL Key Words
Joins, Stored Procedures, Constraints, Triggers
Cursors /Loops / IF Else / Try Catch, Index
Data Manipulation Language (Advanced)
Constraints, Triggers,
Views, Index Advanced
1. Introduction to Bigdata
Introduction and relevance
Uses of Big Data analytics in various industries like Telecom, E- commerce, Finance and Insurance etc.
Problems with Traditional Large-Scale Systems
2. Hadoop (Big Data) Ecosystem
Motivation for Hadoop
Different types of projects by Apache
Role of projects in the Hadoop Ecosystem
Key technology foundations required for Big Data
Limitations and Solutions of existing Data Analytics Architecture
Comparison of traditional data management systems with Big Data management systems
Evaluate key framework requirements for Big Data analytics
Hadoop Ecosystem & Hadoop 2.x core components
Explain the relevance of real-time data
Explain how to use big and real-time data as a Business planning tool
3. Building Blocks
Quick tour of Java (As Hadoop is Written in Java , so it will help us to understand it better)
Quick tour of Linux commands ( Basic Commands to traverse the Linux OS)
Quick Tour of RDBMS Concepts (to use HIVE and Impala)
Quick hands on experience of SQL.
Introduction to Cloudera VM and usage instructions
4. Hadoop Cluster Architecture Configuration Files
Hadoop Master-Slave Architecture
The Hadoop Distributed File System - data storage
Explain different types of cluster setups (Fully distributed/Pseudo etc.)
Hadoop Cluster set up - Installation
Hadoop 2.x Cluster Architecture
A Typical enterprise cluster Hadoop Cluster Modes
5. Hadoop Core Components HDFS & Map Reduce (YARN)
6. HDFS Overview & Data storage in HDFS
Get the data into Hadoop from local machine (Data Loading Techniques) - vice versa
MapReduce Overview (Traditional way Vs. MapReduce way)
Concept of Mapper & Reducer
Understanding MapReduce program skeleton
Running MapReduce job in Command line/Eclipse
Develop MapReduce Program in JAVA
Develop MapReduce Program with the streaming API
Test and debug a MapReduce program in the design time
How Partitioners and Reducers Work Together
Writing Customer Partitioners Data Input and Output
Creating Custom Writable and Writable Comparable Implementations
7. Data Integration Using Sqoop and Flume
Integrating Hadoop into an existing Enterprise
Loading Data from an RDBMS into HDFS by Using Sqoop
Managing Real-Time Data Using Flume
Accessing HDFS from Legacy Systems with FuseDFS and HttpFS
Introduction to Talend (community system)
Data loading to HDFS using Talend
8. Data Analysis using PIG
Introduction to Hadoop Data Analysis Tools
Introduction to PIG - MapReduce Vs Pig, Pig Use Cases
Pig Latin Program & Execution
Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF
Use Pig to automate the design and implementation of MapReduce applications
Data Analysis using PIG
9. Data Analysis using HIVE
Introduction to Hive - Hive Vs. PIG - Hive Use Cases
Discuss the Hive data storage principle
Explain the File formats and Records formats supported by the Hive environment
Perform operations with data in Hive
Hive QL: Joining Tables, Dynamic Partitioning, Custom MapReduce Scripts
Hive Script, Hive UDF
10. Data Analysis Using Impala
Introduction to Impala & Architecture
How Impala executes Queries and its importance
Hive vs. PIG vs. Impala
Extending Impala with User Defined functions
Improving Impala performance
11. NoSQL Database Hbase
Introduction to NoSQL Databases and Hbase
HBase v/s RDBMS, HBase Components, HBase Architecture
HBase Cluster Deployment
12. Hadoop Other Analytics Tools
Introduction to role of R in Hadoop Eco-system
Introduction to Jasper Reports & creating reports by integrating with Hadoop
Role of Kafka & Avro in real projects
13. Other Apache Projects
Introduction to Zookeeper - ZooKeeper Data Model, Zookeeper Service
Introduction to Oozie - Analyze workflow design and management using Oozie
Design and implement an Oozie Workflow
Introduction to Storm
Introduction to Spark
14. Spark
What is Apache Spark?
Using the Spark Shell
RDDs (Resilient Distributed Datasets)
Functional Programming in Spark
Working with RDDs in Spark
A Closer Look at RDDs
Key-Value Pair RDDs
Other Pair RDD Operations
15. Final project
Real World Use Case Scenarios
Understand the implementation of Hadoop in Real World and its benefits.
Final project including integration various key components
Follow-up session: Tips and tricks for projects, certification and interviews etc

Who should attend

People who have good conceptual knowledge in Core Java and SQL who are looking for an
Real World Use Case Scenarios
Understand the implementation of Hadoop in Real World and its benefits


There are no pre-requisites for this course.
Basic knowledge of Core Java and SQL is advantageous.

What you need to bring

We will provide you all the necessary things to learn the Core Java, you don't need to bring anything with you for this course.
If you wish, you can bring your laptops.

Key Takeaways

Overview of Java
Unix History & Over View
Introduction to SQL, Data Definition Language (DDL)
Uses of Big Data analytics
Real World Use Case Scenarios
Understand the implementation of Hadoop in Real World and its benefits.
Final project including integration various key components
Follow-up session: Tips and tricks for projects, certification and interviews etc


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