Big Data & Hadoop Training
Free
1.21GWS training program helps you master Big Data Hadoop and Spark as well as master Hadoop Administration withreal-time industry-oriented case-study projects. In this Big Data course, you will master MapReduce, Hive, Pig, Sqoop, Oozie and Flume and work with Amazon EC2 for cluster setup, Spark framework and RDD, Scala and Spark SQL, Machine Learning using Spark, Spark Streaming, etc.
Training Duration – 6 Days
Every Day Two Module will be covered. Module 1 before Break and Module 2 After Lunch Break.
All Module is having Practical Expects.
REGISTER
GROUP OF 3 OR MORE [USD 800 PER PARTICIPANT]
INDIVIDUAL STANDARD PRICE [USD 1000 PER PARTICIPANT]
Course Features
- Lectures 90
- Quizzes 0
- Duration 6 Days
- Skill level All levels
- Language Big Data & Hadoop
- Students 77
- Certificate No
- Assessments Yes
You have 6 days remaining for the course
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Day 1 : Module 1 - Hadoop Architecture and HDFS
- • Introduction of Big Data
- • Hadoop Architecture
- • Configuration files in a Hadoop
- • LAB:: Data Loading Techniques, how to setup single node
- • Hadoop 2.x Cluster Architecture
- • Hadoop 2.x Configuration Files
- • Federation and High Availability
- • Production Hadoop Cluster
- • Demo Hadoop Cluster Modes
- • LAB ::Common Hadoop Shell Commands
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Day 1 : Module 2
- • YARN MR Application Execution Flow
- • YARN Workflow
- • Anatomy of MapReduce Program
- • Demo on MapReduce
- • Input Splits, Relation between Input Splits and HDFS Blocks
- • Map Reduce Algorithm
- • Hadoop MapReduce Framework and the working of MapReduce on data stored in HDFS
- • Input Splits in MapReduce,Combiner&Partitioner
- • Demos on MapReduce using different data sets.
- • Hadoop Cluster Mode Deployment
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Day 2 : Module 1 - Hive
- • 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
- • Hive Tables(Managed Tables and External Tables), Importing Data, Querying Data, Managing Outputs
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Day 2 : Module 2
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Day 3 : Module 1 - PIG
- • 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
- • Shell and Utility Commands
- • Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Specialized joins in Pig, Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function, Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution )
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Day 3 : Module 2
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Day 4 : Module 1 - Flume
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Day 4 : Module 2 - HBASE
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Day 5 : Module 1 - Scala
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Day 5 : Module 2
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Day 6 : Module 1
No items in this section -
RDD - Core Of Spark
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Execution In Spark (Behind the scenes)
- • First Program In Spark
- • What are Dependencies and Why They are Important
- • Program to Execution
- • Caching Data In Spark
- • Fault Tolerance
- • RDD Operations – transformations
- • Spark architecture considering Yarn
- • Spark functional operations like map, map partition reduce etc
- • RDDs (Operations, Transformation, Actions)
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Day 6 : Module 2
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Resource Management
