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ACLM Institute of Professional Studies

Hadoop Training

Hadoop Training Certifications

03 MonthsOnline / OfflineAdvancedACLM CertificationACLM Certification
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Big Data Hadoop refers to the large and complex set of data that are difficult to process using traditional processing systems It comprises of volume of data, velocity at which the data is created, and variety in the data. Online Hadoop Training is meant to solve all these problems. ACLM conducted various programs / seminars through online regarding Hadoop training. Join now and get the advantages.
COURSE OVERVIEW

Course Overview

ACLM Institute of Professional Studies presents Advanced Hadoop Training, a three-month Online / Offline programme designed around Hadoop architecture, HDFS, MapReduce, Pig, Hive, HBase, YARN, Oozie and practical Big Data Analytics. The programme combines structured instruction, ACLM notes, assignments and project work. Training is available online for learners in India and international markets, and offline in Greater Noida West, Greater Noida, Noida, Ghaziabad and Delhi NCR.

WHY THIS COURSE

Why Learn This Course?

Hadoop provides a foundation for working with large and complex datasets through distributed storage and processing. This course develops a structured understanding of the Hadoop ecosystem and connects core concepts with practical activities such as cluster setup, MapReduce programming, data loading, analytics, indexing, job scheduling and a real-life Big Data Analytics project.

LEARNING OUTCOMES

What You Will Learn

  • Understand Big Data structures, characteristics and limitations of traditional data-processing approaches.
  • Explain Hadoop 2.x architecture and the roles of HDFS, YARN and MapReduce.
  • Work with HDFS and understand distributed storage, data blocks, replication and fault tolerance.
  • Set up a Hadoop cluster in a guided learning environment.
  • Develop basic, complex and advanced MapReduce programmes.
  • Study data loading techniques using Sqoop and Flume.
  • Perform data analysis using Pig and Hive.
  • Use advanced Hive concepts and understand indexing-related practices.
  • Implement HBase and integrate HBase with MapReduce.
  • Schedule Hadoop jobs using Oozie.
  • Apply practical development best practices for Hadoop projects.
  • Complete a real-life project focused on Big Data Analytics.
SKILLS

Skills You Will Gain

  • HDFS and distributed storage fundamentals
  • Hadoop 2.x architecture understanding
  • MapReduce programming and optimisation fundamentals
  • Hadoop cluster setup and basic administration workflow
  • Data ingestion using Sqoop and Flume
  • Pig Latin data transformation and analytics
  • Hive-based querying and data analysis
  • Advanced Hive and HBase usage
  • HBase and MapReduce integration
  • Oozie workflow and job scheduling
  • Hadoop development best practices
  • Project planning and implementation for Big Data Analytics
WHO SHOULD JOIN

Who Should Join?

This advanced programme is intended for Analytics Professionals, BI/ETL/DW Professionals, Project Managers, Testing Professionals, Mainframe Professionals, Software Developers and Architects, graduates aiming to build a career in Big Data, and technical or blog writers.

PREREQUISITES

Prerequisites

Basic Big Data Concepts. Familiarity with programming, databases or data-processing workflows can support learning, but the supplied prerequisite for the course is basic Big data knowledge.

TRAINING APPROACH

Training Methodology

ACLM delivers the programme through instructor-led training, online one-to-one learning and group training. The course includes structured explanations, demonstrations, guided exercises, ACLM notes, assignments and practical projects. Online delivery serves learners in India and international markets. Offline training is available at Greater Noida West, Greater Noida, Noida, Ghaziabad and Delhi NCR.

CURRICULUM

Detailed Course Syllabus

ModuleTopics / Lessons
Module 1: Big Data Structures, Characteristics and Limitations
Establish the conceptual foundation for Hadoop by examining data structures, large-scale data characteristics and the limitations of traditional processing approaches.
  • Understanding Big Data Structures — Explore structured, semi-structured and unstructured data and how data variety affects processing design. 60 min
  • Volume, Velocity and Variety — Examine the major characteristics of Big Data and relate them to storage and analytics requirements. 60 min
  • Limitations of Traditional Systems — Identify common challenges involving scalability, cost, data growth and processing performance. 60 min
  • Hadoop as a Distributed Data Platform — Understand the purpose of Hadoop and the role of distributed storage and parallel processing. 60 min
Module 2: Hadoop Architecture and HDFS
Learn the Hadoop 2.x architecture and build a practical understanding of HDFS, its components, storage model and operational workflow.
  • Hadoop 2.x Architecture Overview — Study the relationship between HDFS, YARN, MapReduce and the wider Hadoop ecosystem. 75 min
  • HDFS Components and Roles — Understand the responsibilities of NameNode, DataNode and supporting HDFS services. 75 min
  • Blocks, Replication and Fault Tolerance — Learn how HDFS stores data, maintains replicas and supports reliable distributed access. 75 min
  • HDFS Commands and File Operations — Practise creating directories, transferring files, listing data and managing HDFS paths. 90 min
  • Guided Hadoop Cluster Setup — Follow a structured setup exercise covering configuration concepts and basic cluster validation. 120 min
Module 3: Hadoop MapReduce Framework – I
Introduce the MapReduce programming model and its execution stages through practical examples.
  • MapReduce Programming Model — Understand the Mapper, Reducer and driver roles in a distributed processing job. 75 min
  • Input, Output and Data Flow — Trace how input data is divided, processed and written to output. 75 min
  • Key-Value Pairs and Data Types — Work with keys, values and common data representations used in MapReduce. 75 min
  • Writing a Basic MapReduce Programme — Develop and execute a simple MapReduce task with guided validation. 120 min
  • Job Execution and Output Review — Inspect job output and interpret basic execution information. 60 min
Module 4: Hadoop MapReduce Framework – II
Extend MapReduce knowledge with joins, counters, partitioning and more complex processing patterns.
  • Combiner and Partitioner Concepts — Understand how combiners and partitioners influence data movement and reducer assignment. 75 min
  • Sorting and Grouping Behaviour — Study shuffle, sort and grouping stages in the MapReduce execution flow. 75 min
  • Counters and Job Monitoring — Use counters and execution information to review processing behaviour and outcomes. 60 min
  • MapReduce Joins — Explore join patterns for combining related datasets in distributed processing. 120 min
  • Complex MapReduce Exercise — Implement a multi-stage processing task and validate its output. 120 min
Module 5: Advanced MapReduce
Develop more effective MapReduce solutions through advanced patterns, performance considerations and implementation review.
  • Advanced MapReduce Design Patterns — Plan multi-step processing solutions for practical data-analysis requirements. 90 min
  • Handling Large-Scale Input — Review input organisation, task design and considerations for distributed execution. 75 min
  • Performance and Resource Considerations — Identify factors affecting processing efficiency and resource use within the Hadoop framework. 90 min
  • Debugging and Output Validation — Use systematic checks to locate errors and validate results from complex jobs. 75 min
  • Advanced MapReduce Assignment — Complete an assignment that applies advanced processing concepts to a defined dataset. 120 min
Module 6: Data Loading with Sqoop and Flume
Learn the course-relevant data-ingestion approaches used to bring data into the Hadoop ecosystem.
  • Data Ingestion in Hadoop — Understand why ingestion design matters and how source systems connect with Hadoop workflows. 60 min
  • Sqoop Concepts and Relational Data Transfer — Study Sqoop-based movement of data between relational sources and Hadoop storage. 90 min
  • Sqoop Import and Export Workflow — Practise planning import and export operations and reviewing transferred data. 90 min
  • Flume Architecture and Data Flow — Understand Flume sources, channels and sinks for collecting and transporting data. 90 min
  • Ingestion Exercise and Validation — Complete a guided data-loading task and check the resulting HDFS data. 90 min
Module 7: Pig for Hadoop Data Analytics
Use Pig as a data-flow and transformation tool for preparing and analysing large datasets.
  • Pig Architecture and Use Cases — Understand Pig's role in Hadoop analytics and the concept of data-flow processing. 60 min
  • Pig Latin Fundamentals — Learn the structure of Pig Latin statements and basic data-flow operations. 90 min
  • Loading, Filtering and Grouping Data — Build transformations using common loading, filtering and grouping operations. 120 min
  • Joining and Aggregating Datasets — Perform joins and aggregations for practical analysis tasks. 120 min
  • Pig Analytics Assignment — Develop a complete Pig script for a guided analytics requirement. 120 min
Module 8: Hive for Hadoop Data Analytics
Build practical SQL-oriented analytics skills with Hive, from table creation through query development.
  • Hive Architecture and Role — Understand Hive's position in the Hadoop ecosystem and its use for analytical querying. 60 min
  • Databases, Tables and Schemas — Create and manage logical structures for organising Hadoop data. 90 min
  • HiveQL Query Fundamentals — Write queries using selection, filtering, grouping and aggregation concepts. 120 min
  • Partitioning and Bucketing Concepts — Examine data-organisation techniques that support query design and management. 90 min
  • Hive Analytics Exercise — Develop queries for a defined dataset and interpret the analytical output. 120 min
Module 9: Advanced Hive and HBase
Progress from standard Hive usage to advanced query practices and the foundations of HBase-based storage.
  • Advanced Hive Querying — Apply nested queries, joins and advanced analytical query patterns. 120 min
  • Hive Performance and Data Organisation — Review practical considerations for table design, partitions and query execution. 90 min
  • HBase Architecture and Data Model — Understand HBase tables, rows, column families and key-based access. 90 min
  • HBase Shell and Data Operations — Practise creating structures and performing basic HBase data operations. 120 min
  • Hive and HBase Use-Case Comparison — Compare analytical querying and key-based access for different data requirements. 60 min
Module 10: Advanced HBase and MapReduce Integration
Work with advanced HBase usage and connect HBase data operations with MapReduce processing.
  • Advanced HBase Usage — Explore data modelling, access patterns and practical management considerations. 90 min
  • HBase Indexing Concepts — Understand indexing-related practices and their relevance to data access. 90 min
  • HBase and MapReduce Integration — Learn how MapReduce can process or interact with HBase data. 120 min
  • Integration Exercise — Implement a guided HBase and MapReduce integration task and review the output. 120 min
  • Advanced HBase Assignment — Complete an assignment covering advanced usage and indexing considerations. 120 min
Module 11: YARN, Oozie and Hadoop Project Workflow
Understand resource management, schedule Hadoop jobs and organise an end-to-end project workflow.
  • YARN Concepts and Architecture — Study YARN's role in resource management and application execution in Hadoop 2.x. 90 min
  • Oozie Architecture and Workflow Definitions — Understand Oozie workflows, actions and dependencies. 90 min
  • Scheduling Hadoop Jobs with Oozie — Create a guided scheduling flow for Hadoop processing tasks. 120 min
  • Workflow Monitoring and Troubleshooting — Review execution status, identify common workflow issues and validate outputs. 90 min
  • Project Planning and Component Selection — Plan the final Big Data Analytics project and select suitable Hadoop components. 90 min
Module 12: Real-Life Big Data Analytics Project
Consolidate the programme through a practical project covering design, implementation, testing and presentation of a Hadoop-based analytics workflow.
  • Project Requirement and Dataset Review — Define the project objective, inspect the available data and identify processing requirements. 90 min
  • Solution Architecture and Data Flow — Design the movement of data through ingestion, storage, processing and analytics stages. 120 min
  • Implementation Sprint — Build the selected Hadoop workflow using the appropriate ecosystem components. 180 min
  • Testing, Validation and Best-Practices Review — Test processing logic, validate outputs and review implementation against Hadoop development practices. 120 min
  • Project Documentation and Presentation — Document the solution, explain design decisions and present the completed project. 120 min
HANDS-ON EXPERIENCE

Practical Projects

  • Oozie Job Scheduling Workflow — Create and schedule a Hadoop processing workflow using Oozie. The activity covers workflow sequencing, job dependencies, execution review and basic troubleshooting. Tools: Hadoop, Oozie, HDFS, MapReduce
  • Hadoop Development Best-Practices Exercise — Apply practical development practices while designing a Hadoop data-processing task, including input organisation, processing logic, output validation and review of implementation choices. Tools: HDFS, MapReduce, YARN, Hadoop development environment
  • Real-Life Big Data Analytics Project — Complete a guided project that brings together distributed storage, data ingestion, processing and analytics. Learners define the processing flow, select suitable Hadoop components, implement the solution and review the results. Tools: HDFS, MapReduce, Pig, Hive, HBase, YARN and Oozie
CAREER APPLICATION

Career Opportunities

The course develops Hadoop and Big Data Analytics skills relevant to work involving distributed data storage, data processing, ETL/DW workflows, analytics, software development, testing and technical project work. It is also suitable for professionals and graduates who want to build practical knowledge of the Hadoop ecosystem. Employment or placement outcomes are not guaranteed by this course.

CERTIFICATION

Certification

Learners who complete the applicable course requirements may receive an ACLM Certificate of Participation. The certificate is issued by ACLM Institute of Professional Studies; no additional accreditation or university affiliation is claimed.

FAQ

Frequently Asked Questions

What is the duration of ACLM Hadoop Training?

The programme duration is 03 Months.

Is the course available online and offline?

Yes. The training modes are Online / Offline. Online learning is available for India and international markets. Offline training is available in Greater Noida West, Greater Noida, Noida, Ghaziabad and Delhi NCR.

What is the level of this course?

Hadoop Training is an Advanced-level programme.

What prerequisite is required?

The stated prerequisite is Basic Big Data Concepts.

Who can join this Hadoop programme?

The course is designed for Analytics Professionals, BI/ETL/DW Professionals, Project Managers, Testing Professionals, Mainframe Professionals, Software Developers and Architects, graduates aiming to build a career in Big Data, and technical or blog writers.

Does the course include practical work?

Yes. The programme includes ACLM notes, assignments, Oozie scheduling work, Hadoop development best-practices exercises and a real-life Big Data Analytics project.

Which Hadoop technologies are covered?

The syllabus covers Hadoop architecture and HDFS, MapReduce, Pig, Hive, advanced Hive, HBase, YARN, Oozie, Sqoop and Flume, along with project implementation.

Will I receive a certificate?

Learners who complete the applicable course requirements may receive an ACLM Certificate of Participation.

Is the course recorded?

No. The supplied course facts specify online and offline training, and recorded training is not available.

Does ACLM guarantee a job or placement after the course?

No employment or placement guarantee is stated for this course. The programme focuses on developing practical Hadoop and Big Data Analytics skills.

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