Big Data Hadoop Training in Pune-Mumbai-Thane-online-classroom-classes. Why This Course? Hadoop is an open-source software framework that supports the processing and storage of extremely large data sets in a distributed computing environment. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are a common occurrence and should be automatically handled by the framework. McKinsey predicts that by 2018 there will be a shortage of 1.5M data expertsAverage Salary of Big Data Hadoop Developers is $135k (Indeed.com salary data) .
Clasroom training batch schedules:
|Pune/Mumbai/Thane||60 hrs||2018-12-01||10:00||Weekend||Quick Enquiry|
Event batch schedules:
|Location||Day/Duration||Start Date||₹ Price||Book Seat|
|Mumbai/Pune||5 Days||2018-12-01||15000||Enroll Now|
Online training batch schedules:
|Mode||Day/Duration||Start Date||End Date||₹ Price||Book Seat|
|online||50 hrs||2018-12-01||2019-01-01||20000||Enroll Now|
Big Data Hadoop Training in Pune Hadoop 2.X- Bigdata Analytics Duration: 60 Hours Prerequisites • No specific programming background needed. Course Content 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 Hadoop - Bigdata 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
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