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Erasure Coding in Hadoop: Reducing Storage Costs Without Losing Data

  In the era of big data, organisations handle massive volumes of information on a daily basis. Enterprises rely on Hadoop for its ability to store and process massive datasets across clusters, making data management more scalable and efficient. However, storing petabytes of data reliably is a costly endeavour. Traditionally, Hadoop relied on data replication, which, while effective in ensuring data availability, significantly inflated storage costs. Enter Erasure Coding — a game-changing technique designed to reduce storage overhead without compromising on data reliability. Whether you’re a tech enthusiast or someone pursuing a data scientist course , understanding erasure coding can provide deeper insights into how modern data systems are evolving to become more cost-efficient and scalable. What is Erasure Coding? As a fault-tolerant technique, Erasure Coding divides data, adds redundancy, and stores it across multiple nodes to safeguard against loss. If some fragments are lost ...
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Federated Learning with Hadoop: Enabling Privacy-Preserving AI Models

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  In today’s data-driven world, artificial intelligence (AI) plays a pivotal role in transforming industries—from healthcare to finance and beyond. However, as AI models become increasingly reliant on large volumes of data, concerns around data privacy and security continue to escalate. Enter Federated Learning —a breakthrough method that enables collaborative machine learning without compromising user data. When combined with Hadoop’s robust distributed framework, federated learning becomes a game-changer for privacy-preserving AI. This blog explores how federated learning works with Hadoop and why this combination is crucial in the era of ethical AI. What is Federated Learning? Federated Learning (FL) is a decentralised machine learning technique where models are trained across multiple devices or servers holding local data samples, without actually sharing the data. Unlike traditional machine learning methods that centralise data for training, FL allows the model to travel to th...
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Future of Data Analysts: How AI Co-Pilots Are Changing the Role

Imagine data analysts as detectives who examine numbers and facts to uncover hidden clues and tell meaningful stories. Now, picture smart computer helpers—called AI Co-Pilots—that assist these detectives by performing some of the routine, repetitive work. This partnership allows data analysts to focus more on the exciting and complex parts of their job. Think of it as working on a huge puzzle representing a big map. The data analyst spends hours sorting the pieces and fitting edges together. The AI Co-Pilot quickly identifies which pieces fit where and suggests placements, but the analyst still controls the final picture. This collaboration accelerates the entire puzzle-building process while preserving the analyst's crucial role. As AI systems grow more intelligent, these helpers won't just take over chores. They'll spark new ideas, find hidden insights faster, and actively collaborate with data detectives. This evolution means data analysts will transform into creative ex...
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