MapReduce produces and procedures huge information on a group of PCs utilizing a parallel, appropriated calculation and accommodating adaptation to non-critical failure. In the event that Hadoop is the sensory system associating a wide range of servers together, then MapReduce is the mind creating and handling data into significant information
The primary target of MapReduce internet preparing system is to show members how to compose MapReduce programs, moving from easy to complex projects by degrees and with a lot of illustrations. It starts with the MapReduce establishment and design process and proceeds onward to hypothesis and practice. Portraying the information stream of the Mapper info and Reducer yield preparing, it gives the 10,000-foot view with respect to the ideas required in MapReduce. It discusses how the program oversees servers and how to connect with the MapReduce API. It demonstrates to oversee employment with the Hadoop CLI and screen them with MCS. It talks about how to function with various information sources on MapReduce and the conveyed store. It likewise incorporates the methods required in running numerous employments and tying.
As the number of associations attempting to tackle the energy of huge information preparing duplicates, the IT divisions are discovering that their centralized servers, intended for organized information, are not ready to deal with the surge of unstructured information. This reality is inciting a move to exceptional stages that can deal with big business workloads, which is the place MapReduce and Hadoop come in. MapReduce and Hadoop offer IT offices the capacity to deal with boundless simultaneous errands and gigantic preparing power with effectiveness and dependability. These are the instruments that are vital for crunching high volumes of unstructured information. In this way, engineers, information designers, database managers, and information examiners, specifically have much to pick up by acing MapReduce.
The normal pay of Hadoop designers working in the huge information industry has been accounted as $135,000. Exploit this worldwide move to huge information and enlist in MapReduce web-based preparing program today.
What is Hadoop? Why Hadoop?
Different types of Components in Hadoop?
HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on…
What is the scope of Hadoop?
Introduction of HDFS
HDFS role in Hadoop
Features of HDFS
Daemons of Hadoop and its functionality
Secondary Name Node
Anatomy of File Wright
Anatomy of File Read
Parallel Copying using DistCp
Basic Configuration for HDFS
How to Store the Data into HDFS
How to Read the Data from HDFS
Accessing HDFS (Introduction of Basic UNIX commands)
The introduction of MapReduce.
Data flow in MapReduce
Sort and shuffle
Understand Difference Between Block and InputSplit
Role of RecordReader
Basic Configuration of MapReduce
MapReduce life cycle
How MapReduce Works
Writing and Executing the Basic MapReduce Program using Java
Submission & Initialization of MapReduce Job.
File Input/Output Formats in MapReduce Jobs
Text Input Format
Key Value Input Format
Sequence File Input Format
NLine Input Format
Word Count Example
Partition MapReduce Program
Side Data Distribution
Distributed Cache (with Program)
Counters (with Program)
Types of Counters
User Defined Counters
Propagation of Counters
Introduction to PIG Data Flow Engine
MapReduce vs. PIG in detail
When should PIG use?
Data Types in PIG
Basic PIG programming
Modes of Execution in PIG
Local Mode and
Operators/Transformations in PIG
PIG UDF’s with Program
Word Count Example in PIG
The difference between the MapReduce and PIG
Use of SQOOP
Connect to mySql database
Joins in SQOOP
Export to MySQL
Export to HBase
HIVE Meta Store
Tables in HIVE
Hive Data Types
Joins in HIVE
HIVE UDF’s and UADF’s with Programs
Word Count Example
Basic Configurations of HBASE
Fundamentals of HBase
What is NoSQL?
HBase Data Model
Table and Row
Column Family and Column Qualifier
Cell and its Versioning
Categories of NoSQL Data Bases
Column Family Database
SQL vs. NOSQL
How HBASE differs from RDBMS
HDFS vs. HBase
Client-side buffering or bulk uploads
HBase Designing Tables
Where to Use?
Configuration On Windows
Inserting the data into MongoDB?
Reading the MongoDB data.
Downloading and installing the Ubuntu12.x
Increasing Decreasing the Cluster size
Monitoring the Cluster Health
Starting and Stopping the Nodes
Introduction to OOZIE
Use of OOZIE
Where to use?
Introduction to Flume
Uses of Flume
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At the end of this course, you will receive a course completion certificate which certifies that you have successfully completed GoLogica training in MapReduce technology.
You will get certified in MapReduce by clearing the online examination with a minimum score of 70%.
To help you prepare for a certification exam, we shall provide you a simulation exam and a practice exam.
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Course Duration - 30 hrs
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Course Duration - 8 Weekends
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Training Mode - Online
Course Duration - 15 Days
Hours - 2hr/Day
Training Mode - Online