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Hadoop with Python

Name: Hadoop with Python
SKU: ET20250128-070
Availability: InStock

Original price was: $35.00.Current price is: $3.00.

Learn how to process big data with Python and Hadoop. Master data engineering techniques for large-scale analytics.

SKU: ET20250128-070 Categories: Data Science & Analytics, E-Books & PDF Guides, Software Development Tags: Analytics, Big Data, Cloud Computing, Data Engineering, Data Processing, Hadoop, Machine Learning, Python

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Description

Hadoop with Python Masterclass is a comprehensive training designed to take you from zero to hero in big‑data processing using the power of Python combined with Apache Hadoop.

Course Overview

In this course, you will learn how to harness the full potential of Hadoop’s distributed storage and processing capabilities using Python. This not only includes setting up and managing Hadoop clusters, but also writing real world data-processing scripts, MapReduce jobs, and leveraging modern Python‑Hadoop integration tools. Ideal for data engineers, analysts, developers, and anyone interested in Big Data — even if your background is purely in Python.

Who Should Enroll?

Python developers who want to transition into Big Data / Data Engineering roles
Data analysts and scientists who need to process very large datasets beyond what Pandas alone can handle
Software engineers looking to add distributed data processing and ETL to their skill set
Anyone interested in understanding how large-scale data storage and processing works in real‑world, production‑level systems

What You’ll Learn

Fundamentals of Big Data and Hadoop ecosystem — HDFS, MapReduce, YARN and cluster architecture :contentReference[oaicite:1]{index=1}
How to install and configure Hadoop (single-node and multi-node) on Linux / Windows
Writing MapReduce jobs using Python via Hadoop Streaming, MRJob, or other Python‑Hadoop bridges :contentReference[oaicite:2]{index=2}
Working with distributed data storage: reading/writing from HDFS using Python
Using modern tools like Pydoop or MRJob to abstract and simplify Hadoop + Python development :contentReference[oaicite:5]{index=5}
Leveraging the Hadoop ecosystem: integrating with data‑analysis workflows, preparing data for analytics or machine learning pipelines
Best practices for performance, fault-tolerance, scalability, and resource management in distributed data processing

Course Outline

Introduction to Big Data & Why Hadoop — need, history, and when to use it.
Hadoop Architecture — HDFS, NameNode/DataNode, YARN, cluster & rack awareness.
Setting Up Your Hadoop Environment (single-node & pseudo-distributed mode).
Understanding MapReduce and Hadoop APIs.
Hadoop Streaming — Running Python MapReduce jobs on Hadoop.
Advanced Python–Hadoop Integration with Pydoop / MRJob.
Data ingestion, ETL pipelines, reading/writing HDFS from Python.
Real-world data processing examples: log analytics, large dataset transformation, batch processing.
Performance optimization, job scheduling, resource tuning.
Preparing processed data for analytics, ML pipelines or warehousing.
Final capstone project — build and run a full data‑processing workflow using Python + Hadoop.

Why This Course Stands Out

While many Big Data courses assume knowledge of Java or Scala, this course is crafted for Python-savvy learners. You don’t need to learn a new programming language — you can directly apply your existing Python knowledge to large-scale data processing. The course emphasizes hands‑on learning: by the end, you’ll be able to build, run, and manage real Hadoop jobs using Python scripts. This makes it perfect for developers, data scientists, or analysts who want to scale beyond single-machine limitations without switching languages.

Recommended Prerequisites

• Basic to intermediate Python programming skills
• Familiarity with command‑line and basic Linux/Windows administration
• Basic understanding of data structures (lists, dictionaries) and data formats (CSV, JSON, text files).

Course Benefits & Outcomes

After completing this course, you will:

Be comfortable with setting up and managing a Hadoop cluster for distributed storage and processing.
Be capable of writing scalable data-processing pipelines using Python.
Handle large datasets — gigabytes to terabytes — efficiently using distributed computing instead of local memory.
Integrate big data workflows into analytics, data science, or machine learning projects.
Boost your resume as a data engineer / big data developer with practical, production‑ready skills.

Explore These Valuable Resources.

Interested in Related Learning Paths?

Enroll Today & Take Your Big Data Skills to the Next Level

Whether you’re a Python developer, data analyst, or someone stepping into the world of Big Data, this “Hadoop with Python Masterclass” will give you the tools, confidence, and practical skills to handle large-scale data processing tasks — all using a language you already love: Python. Join now, build distributed data pipelines, and transform how you work with data forever.