Q. What is the primary challenge faced by traditional relational database management technologies when dealing with big data analytics?

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Q. How do companies like Facebook and Twitter achieve scalability for their MySQL installations?

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Q. What is the main difference between vertical scalability and horizontal scalability with database products?

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Q. What should be considered when constructing Big Data systems on premise?

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Q. What is the advantage of most Big Data systems when it comes to data structure?

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Q. Why should transformations that cause less latency be preferred within the Big Data domain?

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Q. What is the primary consideration for scaling Big Data systems to match data growth patterns?

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Q. Which type of scalability involves adding more capacity to a single machine?

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Q. What does sharding involve in the context of database scalability?

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Q. Which layer is considered the most vital among the three layers in the overall infrastructure for many Internet companies?

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Q. What is used as the scalable file system at the bottom of the Storage & Processing layer?

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Q. What is the purpose of a dataflow programming framework in the Storage & Processing layer?

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Q. Why is debugging large-scale data in Internet firms crucial?

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Q. What is the purpose of capturing provenance data across the workflow in dealing with different data and process granularity?

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Q. What is the major role of knowledge as a required feature for survival?

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Q. What are the triggers for ontology evolution in networked and interlinked environments?

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Q. What is one of the essential requirements in domains where the role of ontologies has become established?

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Q. What is one of the serious disadvantages of ontologies built using current knowledge engineering and management frameworks?

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Q. What is one of the major obstacles for the wider acceptance of semantic technologies in industries?

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Q. How is the selection of the best-fitting knowledge representation currently carried out for processing Big Data semantics?

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