Hadoop Course Fee and Duration
| Track | Regular Track | Weekend Track | Fast Track |
|---|---|---|---|
| Course Duration | 45 - 60 Days | 8 Weekends | 5 Days |
| Hours | 2 hours a day | 3 hours a day | 6+ hours a day |
| Training Mode | Live Classroom | Live Classroom | Live Classroom |
This is an approximate course fee and duration for Hadoop Training. Please contact our team for current Hadoop Training course fee and duration.
Best Hadoop Training in Pitampura Delhi & Best Hadoop Training Institute in Pitampura Delhi
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APTECH Pitampura Delhi- the best HADOOP training institute in Pitampura Delhi provides the best HADOOP training in Pitampura Delhi based on the latest industry requirements and standards, Ans thus helps the students to acquire their dream jobs and best career in software development at various MNCs with attractive salary packages. APTECH Pitampura Delhi, Best computer training institute in Pitampura Rohini Delhi being the best HADOOP training provider is one of the Known HADOOP training institutes in Pitampura, Rohini Delhi which offers best in depth knowledge of HADOOP development and Best HADOOP Project Training with hands on practical knowledge helping the trainees to enhance their skills as per the industry requirements. The HADOOP course content here is designed and developed by professionals and it covers both the basic & advanced level HADOOP courses in Pitampura Delhi. Aptech Pitampura Delhi have Best Computer Trainers and they are matter specialists and corporate professionals with best of experience in managing, creating and directing the real-time HADOOP projects conduct HADOOP training courses in Pitampura Delhi, at APTECH Pitampura Delhi. The Aptech Pitampura Delhi a blend of academic learning and practical sessions including project workso as to give the learners best exposure in the process of HADOOP certification training course which further helps to transforms the learners into skilled software developers who easily recruited/acquire job within the industry and have promising future.
At APTECH Pitampura Delhi the HADOOP course syllabus includes the following topics: HADOOP Language Environment, Writing HADOOP Classes, Essentials of Object-Oriented Programming, Exception Handling, Packages, Java Fundamentals, Multithreaded Programming, developing Java APPS, Java Util Package / Collections Framework, I/O Operations in HADOOP, java framework struts 2, Spring MVC framework, Tapestry, Apache Axis, Hibernate, JDOM, Java Applet, Google Web Toolkit (GWT), SiteMesh, Servlet API, Java training on real-time projects along with best placement training.
APTECH Pitampura Delhi’s HADOOP certification course in Pitampura Delhi has been designed by professional as per latest industry requirements keeping in view the advanced HADOOP course content and whole syllabus is based on the professional requirement of the student in order to help them get placed in Multinational companies and achieve their career goals.
APTECH Pitampura Delhi is one of the Oldest HADOOP training centers in Pitampura Delhi with best infrastructure and lab facilities. APTECH Pitampura Delhi trains and develops best of HADOOP learners transforming them into thorough professionals at very reasonable HADOOP training fees, keeping in mind the training and course content requirement of each student.
APTECH Pitampura Delhi is the best HADOOP training institutes in Pitampura Delhi that provides 100% placement support and training to all students at affordable HADOOP training fees. APTECH Pitampura Delhi also provides fast track HADOOP training classes in Pitampura Delhi.
Course Content and Syllabus for Hadoop Training in Pitampura Delhi
Hadoop Course Content
- Hadoop Overview, Architecture Considerations, Infrastructure, Platforms and Automation
Use case walkthrough
- ETL
- Log Analytics
- Real Time Analytics
Hbase for Developers :
NoSQL Introduction
- Traditional RDBMS approach
- NoSQL introduction
- Hadoop & Hbase positioning
Hbase Introduction
- What it is, what it is not, its history and common use-cases
- Hbase Client – Shell, exercise
Hbase Architecture
- Building Components
- Storage, B+ tree, Log Structured Merge Trees
- Region Lifecycle
- Read/Write Path
Hbase Schema Design
- Introduction to hbase schema
- Column Family, Rows, Cells, Cell timestamp
- Deletes
- Exercise - build a schema, load data, query data
Hbase Java API – Exercises
- Connection
- CRUD API
- Scan API
- Filters
- Counters
- Hbase MapReduce
- Hbase Bulk load
Hbase Operations, cluster management
- Performance Tuning
- Advanced Features
- Exercise
- Recap and Q&A
MapReduce for Developers
Introduction
- Traditional Systems / Why Big Data / Why Hadoop
- Hadoop Basic Concepts/Fundamentals
Hadoop in the Enterprise
- Where Hadoop Fits in the Enterprise
- Review Use Cases
Architecture
- Hadoop Architecture & Building Blocks
- HDFS and MapReduce
Hadoop CLI
- Walkthrough
- Exercise
MapReduce Programming
- Fundamentals
- Anatomy of MapReduce Job Run
- Job Monitoring, Scheduling
- Sample Code Walk Through
- Hadoop API Walk Through
- Exercise
MapReduce Formats
- Input Formats, Exercise
- Output Formats, Exercise
Hadoop File Formats
MapReduce Design Considerations
Hadoop File Formats
MapReduce Algorithms
- Walkthrough of 2-3 Algorithms
MapReduce Features
- Counters, Exercise
- Map Side Join, Exercise
- Reduce Side Join, Exercise
- Sorting, Exercise
Use Case A (Long Exercise)
- Input Formats, Exercise
- Output Formats, Exercise
MapReduce Testing
Hadoop Ecosystem
- Oozie
- Flume
- Sqoop
- Exercise 1 (Sqoop)
- Streaming API
- Exercise 2 (Streaming API)
- Hcatalog
- Zookeeper
HBase Introduction
- Introduction
- HBase Architecture
VIEW Types
- Default Views
- Overriden Views
- Normal Views
MapReduce Performance Tuning
Development Best Practice and Debugging
Apache Hadoop for Administrators
Hadoop Fundamentals and Architecture
- Why Hadoop, Hadoop Basics and Hadoop Architecture
- HDFS and Map Reduce
Hadoop Ecosystems Overview
- Hive
- Hbase
- ZooKeeper
- Pig
- Mahout
- Flume
- Sqoop
- Oozie
Hardware and Software requirements
- Hardware, Operating System and Other Software
- Management Console
Deploy Hadoop ecosystem services
- Hive
- ZooKeeper
- HBase
- Administration
- Pig
- Mahout
- Mysql
- Setup Security
Enable Security – Configure Users, Groups, Secure HDFS, MapReduce, HBase and Hive
- Configuring User and Groups
- Configuring Secure HDFS
- Configuring Secure MapReduce
- Configuring Secure HBase and Hive
Manage and Monitor your cluster
Command Line Interface
Troubleshooting your cluster
Introduction to Big Data and Hadoop
Hadoop Overview
- Why Hadoop
- Hadoop Basic Concepts
- Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout
- Where Hadoop fits in the Enterprise
- Review use cases
Apache Hive & Pig for Developers
Overview of Hadoop
- Why Hadoop
- Hadoop Basic Concepts
- Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout
- Where Hadoop fits in the Enterprise
- Review use cases
Overview of Hadoop
- Big Data and the Distributed File System
- MapReduce
Hive Introduction
- Why Hive?
- Compare vs SQL
- Use Cases
Hive Architecture – Building Blocks
- Hive CLI and Language (Exercise)
- HDFS Shell
- Hive CLI
- Data Types
- Hive Cheat-Sheet
- Data Definition Statements
- Data Manipulation Statements
- Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
- Built-in Functions
- Union, Sub Queries, Sampling, Explain
Hive Architecture – Building Blocks
- Hive CLI and Language (Exercise)
- HDFS Shell
- Hive CLI
- Data Types
- Hive Cheat-Sheet
- Data Definition Statements
- Data Manipulation Statements
- Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
- Built-in Functions
- Union, Sub Queries, Sampling, Explain
Hive Architecture – Building Blocks
- Hive CLI and Language (Exercise)
- HDFS Shell
- Hive CLI
- Data Types
- Hive Cheat-Sheet
- Data Definition Statements
- Data Manipulation Statements
- Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
- Built-in Functions
- Union, Sub Queries, Sampling, Explain
Hive Usecase implementation -(Exercise)
- Use Case 1
- Use Case 2
- Best Practices
Advance Features
- Transform and Map-Reduce Scripts
- Custom UDF
- UDTF
- SerDe
- Recap and Q&A
Pig Introduction
- Position Pig in Hadoop ecosystem
- Why Pig and not MapReduce
- Simple example (slides) comparing Pig and MapReduce
- Who is using Pig now and what are the main use cases
- Pig Architecture
- Discuss high level components of Pig
- Pig Grunt - How to Start and Use
Pig Latin Programming
- Data Types
- Cheat sheet
- Schema
- Expressions
- Commands and Exercise
- Load, Store, Dump, Relational Operations,Foreach, Filter, Group, Order By, Distinct, Join, Cogroup,Union, Cross, Limit, Sample, Parallel
Use Cases (working exercise)
- Use Case 1
- Use Case 2
- Use Case 3 (compare pig and hive)
Advanced Features, UDFs
Best Practices and common pitfalls
Mahout & Machine Learning
- Mahout Overview
- Mahout Installation
- Introduction to the Math Library
- Vector implementation and Operations (Hands-on exercise)
- Matrix Implementation and Operations (Hands-on exercise)
- Anatomy of a Machine Learning Application
Classification
- Introduction to Classification
- Classification Workflow
- Feature Extraction
- Classification Techniques (Hands-on exercise)
Evaluation (Hands-on exercise)
- Clustering
- Use Cases
- Clustering algorithms in Mahout
- K-means clustering (Hands-on exercise)
- Canopy clustering (Hands-on exercise)
Clustering
- Mixture Models
- Probabilistic Clustering – Dirichlet (Hands-on exercise)
- Latent Dirichlet Model (Hands-on exercise)
- Evaluating and Improving Clustering quality (Hands-on exercise)
- Distance Measures (Hands-on exercise)
Recommendation Systems
- Overview of Recommendation Systems
- Use cases
- Types of Recommendation Systems
- Collaborative Filtering (Hands-on exercise)
- Recommendation System Evaluation (Hands-on exercise)
- Similarity Measures
- Architecture of Recommendation Systems
- Wrap Up
Command Line Interface
Troubleshooting your cluster
Introduction to Big Data and Hadoop
Hadoop Overview
- Why Hadoop
- Hadoop Basic Concepts
- Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout
- Where Hadoop fits in the Enterprise
- Review use cases
Apache Hive & Pig for Developers
Overview of Hadoop
- Why Hadoop
- Hadoop Basic Concepts
- Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout
- Where Hadoop fits in the Enterprise
- Review use cases
Overview of Hadoop
- Big Data and the Distributed File System
- MapReduce
Hive Introduction
- Why Hive?
- Compare vs SQL
- Use Cases
Hive Architecture – Building Blocks
- Hive CLI and Language (Exercise)
- HDFS Shell
- Hive CLI
- Data Types
- Hive Cheat-Sheet
- Data Definition Statements
- Data Manipulation Statements
- Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
- Built-in Functions
- Union, Sub Queries, Sampling, Explain
Hive Architecture – Building Blocks
- Hive CLI and Language (Exercise)
- HDFS Shell
- Hive CLI
- Data Types
- Hive Cheat-Sheet
- Data Definition Statements
- Data Manipulation Statements
- Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
- Built-in Functions
- Union, Sub Queries, Sampling, Explain
Hive Architecture – Building Blocks
- Hive CLI and Language (Exercise)
- HDFS Shell
- Hive CLI
- Data Types
- Hive Cheat-Sheet
- Data Definition Statements
- Data Manipulation Statements
- Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
- Built-in Functions
- Union, Sub Queries, Sampling, Explain
Hive Usecase implementation -(Exercise)
- Use Case 1
- Use Case 2
- Best Practices
Advance Features
- Transform and Map-Reduce Scripts
- Custom UDF
- UDTF
- SerDe
- Recap and Q&A
Pig Introduction
- Position Pig in Hadoop ecosystem
- Why Pig and not MapReduce
- Simple example (slides) comparing Pig and MapReduce
- Who is using Pig now and what are the main use cases
- Pig Architecture
- Discuss high level components of Pig
- Pig Grunt - How to Start and Use
Pig Latin Programming
- Data Types
- Cheat sheet
- Schema
- Expressions
- Commands and Exercise
- Load, Store, Dump, Relational Operations,Foreach, Filter, Group, Order By, Distinct, Join, Cogroup,Union, Cross, Limit, Sample, Parallel
Use Cases (working exercise)
- Use Case 1
- Use Case 2
- Use Case 3 (compare pig and hive)
Advanced Features, UDFs
Best Practices and common pitfalls
Mahout & Machine Learning
- Mahout Overview
- Mahout Installation
- Introduction to the Math Library
- Vector implementation and Operations (Hands-on exercise)
- Matrix Implementation and Operations (Hands-on exercise)
- Anatomy of a Machine Learning Application
Classification
- Introduction to Classification
- Classification Workflow
- Feature Extraction
- Classification Techniques (Hands-on exercise)
Evaluation (Hands-on exercise)
- Clustering
- Use Cases
- Clustering algorithms in Mahout
- K-means clustering (Hands-on exercise)
- Canopy clustering (Hands-on exercise)
Clustering
- Mixture Models
- Probabilistic Clustering – Dirichlet (Hands-on exercise)
- Latent Dirichlet Model (Hands-on exercise)
- Evaluating and Improving Clustering quality (Hands-on exercise)
- Distance Measures (Hands-on exercise)
Recommendation Systems
- Overview of Recommendation Systems
- Use cases
- Types of Recommendation Systems
- Collaborative Filtering (Hands-on exercise)
- Recommendation System Evaluation (Hands-on exercise)
- Similarity Measures
- Architecture of Recommendation Systems
- Wrap Up
