Bigdata Hadoop Online Training | Ameerpet, Hyderabad | Marathahalli, Bangalore | India
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About The Course
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of core concepts will be covered in the course along with implementation on varied industry use-cases. At TiiH we offer the professional big data hadoop training in Ameerpet, Hyderabad, India.
Who should go for this course?
Predictions say 2015 will be the year Hadoop finally becomes a cornerstone of your business technology agenda. To stay ahead in the game, Hadoop has become a must-know technology for the following professionals:
1. Analytics Professionals
2. BI /ETL/DW Professionals
3. Project Managers
4. Testing Professionals
5. Mainframe Professionals
6. Software Developers and Architects
Big Data Hadoop Material Free download, Ameerpet Hyderabad
Please read the below document on Big Data Hadoop concepts and in detailed description.
7. Graduates aiming to build a career in Big Data
Why learn Big Data and Hadoop?
Forrester predicts, CIOs who are late to the Hadoop game will finally make the platform a priority in 2015. Hadoop has evolved as a must-to-know technology and has been a reason for better career, salary and job opportunities for many professionals. Learn Big Data Hadoop in Hyderabad, India and get MNC job.
What are the pre-requisites for this Course?
Knowledge of core java concepts is the pre-requisite for this course in our Hyderabad based online training institute. We provide a complimentary online and classroom course i.e. “Java Essentials for Hadoop” to all the participants who enroll for the Hadoop Training.
Snapshots2. Linux Basics
Commands3. Hadoop Why Hadoop?
Hadoop v/s RDBMS
Brief history of hadoop 4. Setup hadoop Pseudo mode
Installation of java, hadoop
Configurations of hadoop
Hadoop Processes ( NN, SNN, JT, DN, TT)
Common errors when running hadoop cluster, solutions 5. HDFS- Hadoop distributed File System HDFS Design and Architecture
Interacting HDFS using command line
Interacting HDFS using Java APIs
Replica 6. Hadoop Processes Name node
Secondary name node
Data node 7. Map Reduce Developing Map Reduce Application
Phases in Map Reduce Framework
Map Reduce Input and Output Formats
Combiner 8. Joining datasets in Mapreduce jobs Map-side join
Reduce-Side join 9. Map reduce – customization Custom Input format class
Custom Output format class 10. Hadoop Programming Languages I.HIVE
Installation and Configuration
Interacting HDFS using HIVE
Map Reduce Programs through HIVE
Loading, Filtering, Grouping….
Data types, Operators…..
Sample programs in HIVEII. PIG
Installation and Configurations
OVERVIEW HADOOP DEVELOPER 11. Introduction 12. The Motivation for Hadoop Problems with traditional large-scale systems
Requirements for a new approach 13. Hadoop: Basic Concepts An Overview of Hadoop
The Hadoop Distributed File System
How MapReduce Works
Anatomy of a Hadoop Cluster
Other Hadoop Ecosystem Components 14. Writing a MapReduce Program The MapReduce Flow
Examining a Sample MapReduce Program
Basic MapReduce API Concepts
The Driver Code
Hadoop’s Streaming API
Using Eclipse for Rapid Development
The New MapReduce API 15. Common MapReduce Algorithms Sorting and Searching
Machine Learning With Mahout
Term Frequency – Inverse Document Frequency
Hands-On Exercise 16. PIG Concepts.. Data loading in PIG
Data Extraction in PIG
Data Transformation in PIG
Hands on exercise on PIG 17. Hive Concepts. Hive Query Language
Alter and Delete in Hive
Partition in Hive
Joins in Hive.Unions in hive
Industry specific configuration of hive parameters
Authentication & Authorization
Statistics with Hive
Archiving in Hive
Hands-on exercise 18. Working with Sqoop Introduction
Hands-on exercise 19. Working with Flume Introduction
Configuration and Setup
Flume Sink with example
Flume Source with example
Complex flume architecture 20. OOZIE Concepts 21. IMPALA Concepts 22. HUE Concepts 23. HBASE Concepts 24. ZooKeeper Concepts
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