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BIG DATA MAPREDUCE TRAINING

289 Learners 30 Hrs (4.0)

  • Learn the concepts of MapReduce
  • Run MapReduce jobs quickly using Python and MRJob
  • Translate complex analysis problems into multi-stage MapReduce jobs
  • Understand how Hadoop distributes.
  • Learn about other Hadoop technologies.
BIG DATA MAPREDUCE TRAINING

Key Highlights

Live interactive Sessions

24/7 Support

Job Assistance

Mentor Support

Project Based Learning

Recognised Certification

Flexible Batches

23rd December 2024

Monday

6:00 AM to 10 PM

24th December 2024

Tuesday

6:00 AM to 10 PM

25th December 2024

Wednesday

6:00 AM to 10 PM

26th December 2024

Thursday

6:00 AM to 10 PM

23rd Dec

Mon

6:00 AM to 10 PM

24th Dec

Tue

6:00 AM to 10 PM

25th Dec

Wed

6:00 AM to 10 PM

26th Dec

Thu

6:00 AM to 10 PM

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Online self-learning courses offer autonomy, allowing individuals to learn at their own pace. They provide structured training materials with review exercises to enhance understanding. Utilizing multimedia resources like videos and presentations, learners actively engage with the content. while flexibility enables customization of study schedules. This fosters an environment conducive to effective learning and skill development, accommodating personal commitments.

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Mapreduce Course Details

GoLogica offers an extensive online training program for Big Data MapReduce, uniquely designed to provide participants with a comprehensive understanding of MapReduce frameworks and their various applications. MapReduce is a powerful programming model used for processing vast amounts of data through parallel or distributed algorithms. The model consists of two primary functions: the Map function, which processes and filters data, and the Reduce function, which aggregates and summarizes the results.

 

The training program goes beyond MapReduce to introduce learners to other critical components of the Hadoop ecosystem, including Yarn, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark. This holistic approach ensures that participants not only master MapReduce but also gain a robust understanding of how different Hadoop tools interact and complement each other.

 

Participants will delve into the Hadoop Distributed File System (HDFS), learning how to effectively manage storage and resources within a distributed computing environment. The course covers the architecture and various configurations of Flume, a tool designed for efficiently collecting, aggregating, and transporting large volumes of log data.

 

MapReduce excels at handling and processing large datasets across clusters of computers, utilizing parallel, distributed algorithms that ensure fault tolerance and scalability. In the Hadoop ecosystem, Hadoop serves as the nervous system connecting diverse servers, while MapReduce functions as the brain, processing data into valuable insights. This training equips participants with the skills to implement robust data processing solutions, manage big data infrastructure, and derive meaningful information from large datasets, making them valuable assets in any data-driven organization.

Salary Trends:

According to ZipRecruiter, The average salary of a Big Data Mapreduce professional typically ranges from $116k to $125k PA. It’s depending on factors such as experience, location, and specific job responsibilities.

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Mapreduce Curriculum

Hadoop Introduction
What is Hadoop?
Why Hadoop?
Hadoop History
Different types of Components in Hadoop
HDFS
MapReduce
PIG
Hive
SQOOP
HBASE
OOZIE
Flume
Zookeeper
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 Input Split
Role of Record Reader
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 Nile 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

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Learning Options

Mapreduce Self-Paced Learning

Self-Paced Learning

  • 24/7 access to premium quality self-paced high-end learning videos providing enhanced training.
  • Explore the digital learning experience with LMS access.
  • Get access to study materials develop by professionals with years of expertise.

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Led by Industry Experts for Mapreduce

Led by Industry Experts

  • Experienced practitioners providing case studies and best practices to sessions.
  • Regular/Weekend batches meeting the requirements of the students.
  • 24/7 online support and guidance by top industry experts and mentors to solve conceptual doubts.

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Mapreduce Corporate Solutions

Corporate Solutions

  • Access world-class learning experiences developed on industry-designed projects, mentoring, etc.
  • 24/7 online support and guidance by top industry experts and mentors.
  • Top-notch online training by industry experts and self-paced learning with effective guidance.

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Mapreduce Certification

The GoLogica certification is widely acknowledged, enhancing the credibility of your resume and opening doors to high-level positions in leading multinational corporations globally.

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%.

Mapreduce course certificate

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Mapreduce Objectives

You'll learn how to use Hadoop, Hive, Pig, Hbase and Spark, as well as all the theoretical aspects of Big Data architectures. The other modules in this course cover Python programming, databases, Big Data Vitesse and, finally, automation and deployment.

• Software developers, project managers, and architects.
• BI, ETL icon, ETL and Data Warehousing professionals.
• Mainframe and Testing logo Mainframe and testing professionals.
• Business analyst logo Business analysts and analytics professionals.
• DBAs and DB professionals.

This reduces the processing time as compared to sequential processing of such a large data set. With MapReduce, rather than sending data to where the application or logic resides, the logic is executed on the server where the data already resides, to expedite processing.

In MapReduce, a Mapper is responsible for processing input data and generating key-value pairs. It applies a transformation to each input record independently and emits intermediate key-value pairs.
A Reducer, on the other hand, takes the intermediate key-value pairs produced by the Mapper and processes them to produce the final output. It groups together values associated with the same key, performs aggregation or other computations, and emits the final output key-value pairs.

Fault tolerance in MapReduce is achieved through data replication and job recovery mechanisms. Data replication ensures that input data is stored redundantly across multiple nodes, allowing tasks to be rerun on another node if a node fails. Job recovery mechanisms, such as check pointing and task re-execution, enable the system to recover from failures by restarting failed tasks or resuming execution from a known checkpoint.

A MapReduce program in Java involves two main parts: Mapper and Reducer • Mapper: Takes data (key-value pairs), processes it, and emits new key-value pairs. Imagine a splitter and sorter.
• Reducer: Groups values with the same key from the mapper, combines them, and emits final output. Think of a summarizer.

In MapReduce, counters are like little trackers that count how many times things happen during your job. They can be built-in (like number of records processed) or custom-made (like number of errors in a specific step). These counts help you monitor how your job is doing, find problems, and make it run better.

Optimizing MapReduce jobs involves several best practices to improve performance and efficiency:
• Data Locality
• Combiner Functions
• Partitioning
• Compression
• Speculative Execution
• Memory Configuration
• Optimized Input and Output Formats
• Hardware Configuration

Why GoLogica?

10+

Years of Experience

250+

Corporate Clients

750+

Courses

50K+

Careers Transformed

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Visit GoLogica website, locate the 'Certificates' section, follow the instructions to verify your course completion by completing the exam and Get more than 70% marks. And download your certificate.

I'll guide you through the certification process step-by-step, ensuring you're well-prepared and confident in your subject matter by clearing an exam.

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Our mock interviews process involves practice sessions, feedback, and role-playing to enhance candidates' communication skills and confidence in a concise, single-line summary:
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GoLogica certification holds value for those seeking to learn and validate their skills in Logic Apps all over the world.

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Our trainers are highly experienced in respective Field and implementing real-time solutions on different Scenarios and Expert in their professionals.

We record each LIVE class session you undergo through this training and recordings of each session class will be updated in your Cloud.

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GoLogica have a 10+ year’s good track record in the training market. However, it was founded in 2013.

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Self-paced training allows learners to study at their own speed, while Live Online training offers real-time, interactive sessions with an instructor.

Self-paced learning offers flexibility, personalized progress, and the ability to review materials at your own convenience.

Live online training offers real-time interaction, immediate feedback, and networking opportunities, which self-paced learning lacks.

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Timetable flexibility depends on the institution and availability; inquire for options.

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Practice consistently, apply learned skills in real-life scenarios, and seek feedback for improvement.

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Self-paced videos can be classified into beginner, intermediate, advanced, and expert levels.

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Yes, you can but just Inquire about extension options post-training.

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Our trainers are highly experienced in on specific subject matter to teach and uses by real-time solutions on different Scenarios and Expert in their professionals.

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Hear From Our Learners

Mapreduce rated (4.0 / 5) based on 21 reviews.
Komal Jain

Amazing course and it helped me complete my project works sooner and very useful course in my career.
Amruth A

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This helped me to guide my team members in my current project.Really a useful course.
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Excellent course and really guided me through this very clearly. Thanks to GoLogica.
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BigData MapReduce course helps you be confident of your exploit and BigData MapReduce Course gives you a lot of every second opportunities dexterously, this wouldn’t have been realisable without the best set of people in the institution.
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