GoLogica offers an extensive training for “Big Data Mapreduce”. The Mapreduce Online training is one of a kind where you would be given an in-depth knowledge on Mapreduce frameworks and various use cases pertaining to it. Basically Mapreduce is a programming tool for processing huge amounts of data with a parallel or distributed algorithm. As the name suggests, MapReduce model consist of two separate Functions, namely Map-function and Reduce-function.
Further an introduction to various components of hadoop like Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark is given. Understanding the Hadoop Distributed File System and explanation on how to work with them for storage and resource management is explained. Next up the Flume, its architecture and different flume configurations are explained.
MapReduce produces and processes 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.
Analytics Experts
Research Experts
IT Engineers and Analyzers
Project Managers
Information Scientists
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.
Hadoop Introduction What is Hadoop? Why Hadoop? And Hadoop History? 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, Design, HDFS role in Hadoop and its Features Daemons of Hadoop and its functionality Name Node, Secondary Name Node Job Tracker, Data Node, Task Tracker Anatomy of File Wright and File Read Network Topology, Nodes and Racks Data Center Parallel Copying using DistCp Basic Configuration for HDFS Data Organization Blocks and Replication Rack Awareness, Heartbeat Signal How to Store the Data into HDFS and How to Read the Data from HDFS Accessing HDFS (Introduction of Basic UNIX commands), CLI commands
The introduction of MapReduce, MapReduce Architecture Data flow in MapReduce Splits, Mapper, Portioning, Sort and shuffle Combiner, Reducer Understand Difference Between Block and InputSplit Role of RecordReader Basic Configuration of MapReduce and MapReduce life cycle Driver Code, Mapper and Reducer 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, Key Value, Sequence File and NLine Input Format Joins, Map-side and Reducer-side Joins Word Count Example and Partition MapReduce Program Side Data Distribution and Distributed Cache (with Program) Counters (with Program) Types of Counters: Task, Job Counters and User Defined Counters Propagation of Counters, Job Scheduling
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 MapReduce Mode Execution Mechanisms, Grunt Shell, Script Embedded, Operators/Transformations in PIG PIG UDF’s with Program Word Count Example in PIG The difference between the MapReduce and PIG
Use of SQOOP and its commands Connect to mySql database Import and Export Eval, Codegen etc… Joins in SQOOP Export to MySQL and Export to HBase
HIVE Meta Store and HIVE Architecture Tables in HIVE Managed, External, Hive Data, Primitive and Complex Types Partition 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 Key-Value, Document and Column Family Database HBASE Architecture, HMaster Region Servers and Regions MemStore and Store SQL vs. NOSQL How HBASE differs from RDBMS HDFS vs. HBase Client-side buffering or bulk uploads HBase Designing Tables and Operations Get, Scan, Put and Delete
Where to Use? Configuration On Windows Inserting the data into MongoDB? Reading the MongoDB data Downloading and installing the Ubuntu12.x Installing Java and Hadoop Creating Cluster, Increasing Decreasing the Cluster size and Monitoring the Cluster Health Starting and Stopping the Nodes Zookeeper, Introduction to Zookeeper Data Modal, Operations OOZIE Introduction to OOZIE, Use of OOZIE and Where to use? Flume, Introduction to Flume and Uses of Flume Flume Architecture, Master, Collectors and Agents
Our trainers are Highly experienced in BIG DATA MAPREDUCE implementing real-time solutions on different Scenarios and Expert in their professionals.
We record each LIVE class session you undergo through this training and we will share the recordings of each session/class.
Trainer will Provide Detailed installation of required software through LMS to the students we support by providing Training and practical in real time experience with all utilities required for completely understanding of this Training.
Yes, there are some group discount are available only if group contain more than 2 Or more participates.
Basic Hard ware requirement is useful to install the Product
We provide Training in a Real Time Projects Oriented
Yes we will Schedule a Demo Session as per the student convenient by sharing LIVE Online Streaming access either through GoToMeeting or WebEx.
If you are enrolled in classes and you have paid fees, but want to cancel the registration for certain reason, it can be done within 48 hours of initial registration. Please make a note that refunds will be processed within 15 days of prior request
As we are one of the Best BIG DATA MAPREDUCE Online Training Provider we have customer throughout the world wide specially from UK, USA, UAE, Australia, Qatar, Singapore, New Zealand, India, Malaysia, Dubai, Doha, Melbourne, Brisbane, Perth, Wellington, Auckland Middle East Countries and other parts of the world
We are also located in USA Offering BIG DATA MAPREDUCE Online Training in Cities like New York, New jersey, Dallas, Seattle, Baltimore, Tempe, Chandler, Scottsdale, Peoria, Honolulu, Columbus, Raleigh, Nashville, Plano, Toronto, Montreal, Calgary, Edmonton, Saint John, Vancouver, Richmond, Mississauga, Saskatoon, Kingston, Kelowna, Houston, Minneapolis, Los Angeles, San Francisco, San Jose, San Diego, Washington DC, Chicago, Philadelphia, St. Louis, Edison, Jacksonville, Towson, Salt Lake City, Davidson, Murfreesboro, Atlanta, Alexandria, Sunnyvale, Santa Clara, Carlsbad, San Marcos, Franklin, Tacoma, California, Bellevue, Austin, Charlotte, Garland, Raleigh-Cary, Boston, Orlando, Fort Lauderdale, Miami, Gilbert.
In Indian we have customer from Bangalore, Mysore, Hyderabad (Ameerpet), Visage, Chennai, Kolkata, Pune, Mumbai, Delhi, Jaipur, Ahmadabad, Kerala etc…
You can clarify your queries by dialing +91 - 82 9696 0414, +1 (646) 586 - 2969 Or you can send a mail to info@gologica.com. We are ready to clear your enquiries at any time
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
Hours - 1hr/day
Training Mode - Online
Course Duration - 8 Weekends
Hours - 2hr/Day
Training Mode - Online
Course Duration - 15 Days
Hours - 2hr/Day
Training Mode - Online