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CS Professional · Artificial Intelligence, Data Analytics and Cyber Security - Laws and Practice · Internet and Other Technologies

Pune-based Kaveri Retail uses a Hadoop-style framework to store huge log files across many low-cost servers and process them in parallel. Which feature of this design best explains how it handles very large datasets?

Such frameworks distribute both storage and processing across a cluster of many commodity nodes, so large datasets are split up and processed in parallel. This allows scaling by adding nodes, unlike a single high-end server, which limits capacity.

  1. AStoring all data on one high-end server with a single processor
  2. BDistributing data and processing across a cluster of nodesCorrect
  3. CConverting all unstructured data into paper records before analysis
  4. DEncrypting data so that it cannot be processed at all

Explanation

Hadoop-style systems split data across the nodes of a cluster (distributed file system) and run processing in parallel on those nodes, allowing scaling on commodity hardware. A single server is the traditional approach that Big Data frameworks aim to overcome. The other options are not features of such frameworks.

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