Hadoop Essentials – Master Big Data Processing & Distributed Computing
Harness the power of Hadoop, the leading framework for Big Data processing and distributed computing. This tutorial provides a structured, in-depth guide covering Big Data fundamentals, HDFS, MapReduce, and essential Hadoop components, ensuring you gain the expertise to handle large-scale data efficiently.
What You’ll Learn
✔ Big Data Overview – Understand the advantages, challenges, and practical use cases of Big Data in modern industries.
✔ Hadoop Fundamentals – Learn Hadoop architecture, setup for single-node and multi-node clusters, and how Hadoop transforms data processing.
✔ HDFS (Hadoop Distributed File System) – Explore HDFS architecture, high availability, rack awareness, federation, and efficient data storage techniques.
✔ MapReduce Processing – Master MapReduce terminologies, job execution flow, data locality, and detailed data-processing operations.
✔ Core Hadoop Concepts – Gain hands-on knowledge of mappers, reducers, input formats, record readers, partitioning, combiner functions, and speculative execution.
Why Enroll?
🚀 Learn Scalable & Distributed Data Processing – Develop the expertise to handle vast amounts of data efficiently.
💡 Industry-Relevant Applications – Master Hadoop’s real-world use cases and how organizations leverage it for data-driven insights.
⚡ Structured, Hands-On Approach – Gain practical insights into HDFS, MapReduce, and Hadoop components for optimized performance.
By the end of this tutorial, you’ll be fully equipped to process, store, and manage Big Data using Hadoop, unlocking opportunities in data engineering and large-scale analytics.
Course Content
Bigdata Overview
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Big Data Introduction
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Advantages of Big Data
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Four V’s of Big Data
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Challenges of Big Data
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Traditional Approach
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Big data Use Cases
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