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Big Data with Hadoop: HDFS, MapReduce & Ecosystem Tools

Master Hadoop ecosystem technologies including HDFS, MapReduce, Hive, Pig, Spark, and distributed data processing through hands-on learning and real-world big data workflows.

  • Learn Big Data and Hadoop fundamentals through structured skill sprints

  • Build and process scalable big data workloads using Hadoop ecosystem tools

  • Work with Hive, Pig, Spark, and HDFS for distributed data processing

  • Develop practical skills for data ingestion, transformation, and analytics

  • Gain hands-on experience with real-world Hadoop and big data workflows

Target Audience

  • Complete beginners who want a structured introduction to Big Data and Hadoop

  • Students and job seekers preparing for entry-level Big Data and data engineering roles

  • Professionals looking to build skills in distributed data processing and analytics

  • Software developers interested in working with large-scale data systems

  • Anyone interested in learning how to process, store, and analyze big data using Hadoop

Big Data with Hadoop: HDFS, MapReduce & Ecosystem Tools Overview

Big Data with Hadoop is a practical, beginner-friendly program designed to build foundational and intermediate skills in distributed data processing using the Hadoop ecosystem. Learners work with HDFS, MapReduce, Hive, Pig, Spark, and ecosystem tools to process, store, transform, and analyze large-scale datasets through guided skill sprints and real-world big data workflows.

  • Learn Big Data and Hadoop through structured Skill Sprints

  • Work with HDFS, MapReduce, Hive, Pig, Spark, and ecosystem tools

  • Build distributed data processing and analytics workflows

  • Apply big data storage, transformation, and querying techniques

  • Develop hands-on Hadoop skills for modern data engineering environments

Delivered using OCA’s Skill Sprint™ Method with hands-on practice, real-world exercises, and instructor-led feedback.

Prerequisites

The following basic skills are recommended to maximize learning outcomes:

  • Comfort using a computer, file navigation, browser usage, and basic typing

  • Familiarity with Microsoft Office tools is beneficial

  • Basic understanding of databases or SQL concepts is helpful but not mandatory

  • Interest in data processing, distributed systems, and problem-solving

  • Willingness to learn Big Data concepts through hands-on exercises

Outcomes

By the end of this course, you will be able to:

  • Understand core Big Data concepts and Hadoop ecosystem architecture

  • Work with HDFS for distributed data storage and management

  • Build and execute MapReduce workflows for large-scale processing

  • Use Hive and Pig for querying and transforming big data datasets

  • Apply distributed data processing techniques using Apache Spark

  • Integrate Hadoop ecosystem tools for data ingestion and analytics

  • Optimize big data processing workflows for scalability and efficiency

  • Build foundational skills for Big Data engineering and analytics roles

Job Roles & Careers

After completing the program, learners will be better prepared for positions such as:

  • Big Data Engineer

  • Hadoop Developer

  • Data Engineer

  • Big Data Analyst

  • ETL Developer

  • Data Processing Engineer

  • Spark Developer

Curriculum

Learn through focused Skill Sprints built around practical application and real-world tasks.

  • Define Big Data and its key characteristics (Volume, Velocity, Variety)

  • Explain limitations of traditional data systems

  • Identify core Hadoop ecosystem components

  • Understand distributed computing concepts

  • Recognize real-world Big Data use cases across industries

  • Explain HDFS architecture and working principles

  • Understand NameNode and DataNode roles

  • Apply replication and fault tolerance concepts

  • Understand high availability in HDFS

  • Analyze distributed data storage behavior

  • Understand MapReduce architecture and workflow

  • Explain map, shuffle, and reduce phases

  • Manage input and output formats

  • Execute basic MapReduce jobs

  • Analyze job execution flow

  • Use combiners and partitioners effectively

  • Optimize MapReduce job configurations

  • Identify performance bottlenecks

  • Apply best practices for job design

  • Analyze execution logs for optimization

  • Understand Pig architecture and execution model

  • Write Pig Latin scripts

  • Perform data transformations on Hadoop

  • Process structured and semi-structured data

  • Validate transformed datasets

  • Understand Hive architecture and components

  • Write HiveQL queries

  • Create and manage tables

  • Apply partitioning and bucketing

  • Perform data analysis using Hive

  • Optimize complex Hive queries

  • Understand HBase architecture and use cases

  • Compare Hive and HBase usage scenarios

  • Integrate Hive with HBase

  • Explore NoSQL data modeling concepts

  • Understand Spark architecture and components

  • Compare Spark with MapReduce

  • Work with Resilient Distributed Datasets (RDDs)

  • Perform data transformations and actions

  • Execute Spark jobs

  • Understand roles of Kafka, Flink, and Storm

  • Perform data ingestion using Sqoop and Apache NiFi

  • Differentiate batch and real-time processing

  • Design data pipelines

  • Map tools to real-world use cases

  • Work with Spark SQL and DataFrames

  • Understand Spark MLlib fundamentals

  • Perform data analysis using Spark

  • Apply performance tuning techniques

  • Optimize distributed processing workloads

Design and implement a scalable big data processing solution using Hadoop ecosystem tools to solve a real-world business problem.

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$1,099   

  • Instructor-Led: Live Online & In-Class

  • 32 Total Hours

  • Advanced Level

  • Real-World Project

  • Career-Focused

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Why This Course Is in Demand

Organizations across industries rely on Hadoop and big data technologies to process and analyze massive datasets for analytics, reporting, and business intelligence. Hadoop ecosystem skills remain highly valuable for data engineering, distributed data processing, and scalable analytics roles in modern data-driven organizations.

Frequently Asked Questions (FAQs)

This course is ideal for beginners exploring Big Data for the first time, students and job seekers preparing for data engineering roles, and working professionals looking to build skills in distributed data processing. It is suitable for individuals from both technical and non-technical backgrounds seeking structured, hands-on learning.

No prior programming experience is required. The course starts with Big Data fundamentals and progressively introduces Hadoop tools and data processing concepts. Basic computer knowledge and familiarity with data concepts are recommended.

Participants learn Big Data fundamentals, HDFS for distributed storage, MapReduce for data processing, and ecosystem tools such as Hive, Pig, Spark, and HBase. The program also covers data pipelines, batch and real-time processing concepts, and concludes with a real-world Big Data project.

This course supports entry-level roles such as Big Data Engineer, Hadoop Developer, Data Engineer, ETL Developer, and Big Data Analyst. It also serves as a pathway toward advanced Data Engineering and Big Data architecture roles.

Yes. The program is designed to accommodate working professionals seeking to upskill in Big Data and distributed systems. The structured Skill Sprint Method™ ensures efficient learning with guided instruction and practical exercises.

The total duration is 32 hours, consisting of 16 hours of instructor-led live sessions and 16 hours of guided hands-on practice and assignments. This balanced structure ensures both conceptual clarity and practical application.

Yes. This is an instructor-led course delivered in both live online and in-class formats. Participants engage in real-time instruction, demonstrations, and guided exercises.

The course covers Hadoop ecosystem tools including HDFS, MapReduce, Hive, Pig, Spark, HBase, along with data ingestion and integration tools such as Sqoop and Apache NiFi.

Yes. Participants who successfully complete the course and final project will receive a Certificate of Completion from OCA.

Yes. Corporate and group training options are available and can be customized to align with organizational learning objectives and industry use cases.

Registration can be completed through the course page on the OCA website or by contacting the admissions team for enrollment assistance and schedule details.

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