Every class is live, one-on-one and set to your hours.See all 243 courses →
Big Data & Hadoop Development Data & Analytics

Big Data & Hadoop Development

Big Data is data at a scale where normal tools break, and Hadoop is how the industry handles it. This course teaches you the Hadoop ecosystem, writing jobs to process huge datasets, and the developer skills that data-heavy companies hire for. It's a strong, specialised skill in the growing data engineering field.

Duration
2 mths
Format
Live 1-on-1
Level
Professional
Certificate
Osian
Overview

What this course is about

Big Data and Hadoop Development introduces the programming side of distributed data processing. In live 1-on-1 online classes, your teacher connects Hadoop 3 concepts with HDFS, YARN, MapReduce, Hive and Apache Spark. You learn why data is partitioned, how jobs are scheduled and where a pipeline can fail. The course is suitable for computer students, Python or Java developers and working professionals who want practical big data project experience rather than only platform terminology.

Hands-on work can include loading files into HDFS, designing Hive tables, writing MapReduce logic and transforming datasets with Spark DataFrames. You explore schemas, partitions, joins, aggregations, serialisation and basic performance reasoning. Python with PySpark is used where it supports clear experimentation, while Java concepts are discussed for learners who need a JVM-oriented route. The teacher also explains batch pipeline stages, logs and data quality checks.

A project such as customer transaction analysis, sensor event processing or an operations dashboard gives you material for a portfolio discussion. This developer-focused course differs from BigData and Hadoop Admin, which concentrates on cluster setup, security, monitoring and operations. Here the emphasis is on writing transformations and queries that produce useful datasets. Flexible timings help learners balance college or work while building confidence with distributed processing vocabulary.

By the end

What you’ll be able to do

  • Explain HDFS, YARN and distributed processing roles
  • Load, partition and query data with Hive
  • Write MapReduce logic for batch transformations
  • Build Spark DataFrame workflows with PySpark
  • Inspect logs and diagnose pipeline data issues
  • Present a complete Hadoop development project
Syllabus

How the course flows

  1. 1
    Data FoundationsWeek 1

    Data types, spreadsheets and thinking analytically.

  2. 2
    Querying & WranglingWeek 2

    Pulling and cleaning data with SQL and tools.

  3. 3
    VisualisationWeek 3

    Dashboards in Power BI / Tableau that tell a story.

  4. 4
    Statistics & InsightWeek 4

    Core statistics and drawing sound conclusions.

  5. 5
    Analytics ProjectWeeks 5–8

    An end-to-end analysis on a real dataset.

Toolkit

Tools you’ll use

  • Excel
  • SQL
  • Power BI
  • Tableau
  • Python (pandas)
Audience

Who this course is for

  • Computer science students learning data engineering
  • Python developers exploring distributed processing
  • Java programmers moving into Hadoop development
  • Analysts handling datasets beyond single-machine tools
  • Working professionals preparing for big data projects
Where it leads

Roles this opens up

  • Junior Big Data Developer

    Create distributed transformations and queries for data processing teams.

  • Hadoop Application Trainee

    Support MapReduce, Hive or Spark jobs under an experienced engineering team.

  • PySpark Data Engineer

    Prepare and transform large datasets using DataFrame-based processing workflows.

Questions

Before you enrol

Basic programming and SQL knowledge is recommended, with Python offering a gentle route into PySpark exercises. Java learners can also connect the concepts to MapReduce and the wider Hadoop ecosystem.

The practical coverage includes HDFS, YARN, MapReduce, Hive and Apache Spark, with attention to how each fits into a processing workflow. The depth is directed towards writing jobs and transformations.

You study MapReduce to understand the original batch model and use Spark DataFrames for modern transformation practice. This combination helps you compare clarity, execution behaviour and use cases.

You can build a pipeline around transactions, sensor events or operational records, including ingestion, transformation and a useful output dataset. The final project is shaped around data that you can legally and safely practise with.

Development focuses on code, queries, transformations and data outputs. Hadoop Admin focuses on installing and managing clusters, access controls, monitoring, capacity and operational reliability.

Yes, guided practice can use a suitable local or managed learning setup depending on your computer and access. The teacher explains environment limitations so you understand which results are meaningful beyond a small lab.