Hadoop Admin Training in Bangalore
Learn Hadoop Admin Course from Expert Oracle Trainers who are Real-time Working Professionals.
Expert Trainers | Practical Training | Mock Interviews | Placement Assistance | Job Support
Best Hadoop Admin Training in BTM Layout Bangalore
Based on Google and other third-party reviews, Softgen Infotech is recognized as one of the Hadoop Admin Training in BTM Layout Bangalore. We have completed over 2000 HADOOP training sessions with a 100% placement rate, assisting college students. For the past 5 years, the team at Softgen Infotech has been dedicated to providing High-Quality Hadoop Admin Training in BTM Layout Bangalore.
Softgen Infotech’s trainers come from the HADOOP industry and have at least 5 years of experience implementing HADOOP. All HADOOP trainers are real-time specialists who provide hands-on experience with practicals.
Our Hadoop Admin Training in BTM Layout Bangalore is well equipped with labs and good infrastructure to provide you with hands-on training. We also offer HADOOP certification training programmes. We have successfully trained and placed the majority of our students in large MNC firms. Softgen provides HADOOP programmes with 100% placement assistance for our students.
Contact us and let us together build a perfect career for your future!
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Why Choose Softgen Infotech
Softgen Infotech is a leading Training Institute in Bangalore providing Quality Training for Multiple IT Courses with Placement Assistance.
Live & Interactive Sessions
Real Time Training
Practical Classes
Interview & Placement Assistance
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Course Material & Syllabus
Get all Course Content and Syllabus. Get Trained like a Professional. We Provide Course Material, Prepare for Interviews, Arrange Mock Interviews and 100 % Placement Assistance
Hadoop training course content and Syllabus
- Hadoop Overview, Architecture Considerations, Infrastructure, Platforms and Automation
- Use case walkthrough
- ETL
- Log Analytics
- Real Time Analytics
- 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
- 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 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
- MapReduce Performance Tuning
- 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
- 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
- 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 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
- 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
Hadoop Admin Training Course Duration & Batch Timings
Duration : 30 Hours | Version : latest Version |
---|---|
Regular : 1 Hour per day | Fast Track : 2 - 3 Hours per day: 10 days |
Weekdays : Monday - Friday | Weekend : Saturday and Sunday |
Online Training : Available | Class Room Training : Available |
Course Fee : Talk to our Customer Support | Mode of Payment : Talk to our Customer Support |
Frequently Asked Questions for Hadoop Admin Training Course
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