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Hadoop Data Analytics Online Training & Certification Course

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Multisoft Virtual Academy avails the Hadoop Data Analytics online training course to provide Hadoop overview, the Hadoop ecosystem, and Hands-on exercises to learn about data ingest with Hadoop Administration Training Online tools.

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Instructor-led Training Live Online Classes

Suitable batches for you

10 Jun 2023	24	06:00 PM - 09:00 PM	Sat, Sun
17 Jun 2023	24	06:00 PM - 09:00 PM	Sat, Sun
24 Jun 2023	24	06:00 PM - 09:00 PM	Sat, Sun

Course Price At

$ 390

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Instructor-led Training Live Online Classes

10 Jun 2023	24	06:00 PM - 09:00 PM	Sat, Sun
17 Jun 2023	24	06:00 PM - 09:00 PM	Sat, Sun
24 Jun 2023	24	06:00 PM - 09:00 PM	Sat, Sun

Course Price At

$ 390

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Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You’ll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.

Course Price At

$ 390

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Instructor-Led Online Training Parameters

Course Highlights

Duration: 24 Hrs.
Subject Matter Expert
After Training Support
Lifetime e-Learning Access
Recorded Sessions
Free Online Assessments

One to One Training

Hadoop Data Analytics Training Course Syllabus

Curriculum Designed by Experts

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Big Data analytics deals with studying the huge data sets consisting numerous data types to study various market trends, customer graph, and important business information. Hadoop Data Analytics is used by the organizations for implementing the Business Intelligence and data analytic skills on the Big Data.

About this Course
About Big Data
Course Logistics
Introductions

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The Motivation for Hadoop
Hadoop Overview
HDFS
MapReduce
The Hadoop Ecosystem
Lab Scenario Explanation
Hands-On Exercise: Data Ingest with Hadoop Tools

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What Is Pig?
Pig’s Features
Pig Use Cases
Interacting with Pig

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Pig Latin Syntax
Loading Data
Simple Data Types
Field Definitions
Data Output
Viewing the Schema
Filtering and Sorting Data
Commonly-Used Functions
Hands-On Exercise: Using Pig for ETL Processing

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Storage Formats
Complex/Nested Data Types
Grouping
Built-in Functions for Complex Data
Iterating Grouped Data
Hands-On Exercise: Analyzing Ad Campaign Data with Pig

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Techniques for Combining Data Sets
Joining Data Sets in Pig
Set Operations
Splitting Data Sets
Hands-On Exercise: Analyzing Disparate Data Sets with Pig

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Adding Flexibility with Parameters
Macros and Imports
UDFs
Contributed Functions
Using Other Languages to Process Data with Pig
Hands-On Exercise: Extending Pig with Streaming and UDFs

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Troubleshooting Pig
Logging
Using Hadoop’s Web UI
Optional Demo: Troubleshooting a Failed Job with the Web UI
Data Sampling and Debugging
Performance Overview
Understanding the Execution Plan
Tips for Improving the Performance of Your Pig Jobs

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What Is Hive?
Hive Schema and Data Storage
Comparing Hive to Traditional Databases Hive vs. Pig
Hive Use Cases Interacting with Hive

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Hive Databases and Tables
Basic HiveQL Syntax
Data Types
Joining Data Sets
Common Built-in Functions
Hands-On Exercise: Running Hive Queries on the Shell, Scripts, and Hue

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Hive Data Formats
Creating Databases and Hive-Managed Tables
Loading Data into Hive
Altering Databases and Tables
Self-Managed Tables
Simplifying Queries with Views
Storing Query Results
Controlling Access to Data
Hands-On Exercise: Data Management with Hive

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Overview of Text Processing
Important String Functions
Using Regular Expressions in Hive
Sentiment Analysis and N-Grams
Hands-On Exercise (Optional): Gaining Insight with Sentiment Analysis

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Understanding Query Performance
Controlling Job Execution Plan
Partitioning
Bucketing
Indexing Data

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SerDes
Data Transformation with Custom Scripts
User-Defined Functions
Parameterized Queries
Hands-On Exercise: Data Transformation with Hive

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What is Impala?
How Impala Differs from Hive and Pig
How Impala Differs from Relational Databases
Limitations and Future Directions
Using the Impala Shell

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Basic Syntax
Data Types
Filtering, Sorting, and Limiting Results
Joining and Grouping Data
Improving Impala Performance
Hands-On Exercise: Interactive Analysis with Impala

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Comparing MapReduce, Pig, Hive, Impala, and Relational Databases
Which to Choose?

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Hadoop Data Analytics Training Description

Data analysis using Pig and its features
Processing complicated data with the help of Pig use cases
Basic data analysis using the Pig Latin syntax
Using the Data ETL (Extract, Transform and Load) and viewing the schema
Multi-dataset operations by joining data sets in Pig
Relational data analysis with Hive
Using Regular Expressions in Hive

Business analysts
Business intelligence professionals
Data analysts
Data scientists
IT professionals
Project managers
Data Warehousing professionals
Data engineers

No prerequisites
Knowledge of core JAVA and SQL recommended

Hadoop Data Analytics Training Certification

Multisoft Virtual Academy provides a globally recognized training certificate to the participants, after successful completion of a training program. The training certificates are recognized and accepted across the world.

Multisoft Virtual Academy's training certificate comes with lifetime validity.

Aspirants can directly enroll for the desired course from the Book Now Button in the course page. You can also connect on Whatsapp at +91 8130666206 to talk with a training advisor. Multisoft Virtual Academy also offers customized training programs on a wide range of domains and skills.

All training programs offered by Multisoft Virtual Academy are delivered by certified industry experts, who have years of experience in the relevant domains. Multisoft Global Subject Matter Experts impart knowledge on a wide variety of training courses through one –on-one and corporate training sessions.

Multisoft Virtual Academy training certification can help participants stand out in the competitive job market. Since the training certificates are internationally accepted, participants can showcase their skills and knowledge to employers across the world.