- 17 Jun, 2025
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- 2 Mins Read
Understanding Hadoop: Evolution, Architecture, Challenges, and Deployment Models
Evolution of Hadoop
Hadoop was developed by Doug Cutting and Mike Cafarella in 2005 as part of the Apache project, inspired by Google’s MapReduce and Google File System (GFS). It was created to store and process massive datasets using distributed computing on commodity hardware.
Overview
Hadoop is an open-source framework designed to store and process large volumes of data across clusters of computers. It follows a distributed computing model, allowing for high fault tolerance, scalability, and parallel processing of data.
Core Components
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HDFS (Hadoop Distributed File System) – Stores data across multiple machines.
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MapReduce – A programming model for parallel data processing.
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YARN (Yet Another Resource Negotiator) – Manages cluster resources.
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Hadoop Common – Provides shared utilities and libraries.
Challenges with Hadoop
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Complexity – Requires skilled resources for setup and maintenance.
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Latency – Not ideal for real-time processing (better suited for batch jobs).
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Scalability Limits – Scaling beyond a point may increase overhead.
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Security Concerns – Native Hadoop lacks strong data governance and encryption features.
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High Storage Overhead – Uses replication (default 3x) which increases storage costs.
On-Premise vs Cloud: Hadoop Deployment
On-Premise Hadoop Deployment
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Requires dedicated in-house infrastructure and physical servers.
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Involves high upfront capital investment for hardware and setup.
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Full control over data, configurations, and security policies.
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Slower to deploy and scale, as adding resources requires manual setup.
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Needs a skilled IT team for continuous monitoring and maintenance.
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Preferred for sensitive data environments with strict compliance needs.
Cloud-Based Hadoop Deployment
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Uses cloud platforms like AWS EMR, Azure HDInsight, or GCP Dataproc.
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Operates on a pay-as-you-go pricing model—no upfront hardware cost.
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Fast deployment and auto-scaling capabilities.
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Reduced maintenance—cloud provider handles infrastructure.
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Ideal for dynamic, large-scale, or short-term data processing jobs.
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Suitable for businesses that prioritize agility and speed over full control.
At Learnomate Technologies, we don’t just teach tools, we train you with real-world, hands-on knowledge that sticks. Our Azure Data Engineering training program is designed to help you crack job interviews, build solid projects, and grow confidently in your cloud career.
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And hey, if this article got your curiosity going…
👉 Explore more on our blog where we simplify complex technologies across data engineering, cloud platforms, databases, and more.
Thanks for reading. Now it’s time to turn this knowledge into action. Happy learning and see you in class or in the next blog!
Happy Vibes!
ANKUSH😎