Q. How can cloud solutions assist in implementing dynamic computational solutions for Big Data?

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Q. How are Big Data processes typically formalized?

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Q. What role do automation systems play in managing Big Data workflows?

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Q. Why might traditional Workflow Management Systems (WfMS) be inadequate for processing Big Data in real time?

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Q. What is the main characteristic of complex event processing (CEP) in the context of Big Data?

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Q. How does the Large Hadron Collider (LHC) handle the massive amount of data it generates?

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Q. Why is the cloud paradigm considered a desirable feature in Big Data solutions?

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Q. What is a limitation of using public cloud systems for extensive computations on large volumes of data?

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Q. Which project allows experiments on interlinked cluster systems for Big Data solutions?

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Q. What does the term "self-healing" refer to in the context of Big Data systems?

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Q. How can a system achieve self-healing in the event of a server or node failure?

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Q. What happens when a node/storage fails in a cluster with self-healing capabilities?

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Q. What is the BASE property in the context of NoSQL databases?

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Q. What is the primary goal of the CAP theorem in distributed storage systems?

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Q. Why is it challenging to implement a fault-tolerant BASE architecture for Big Data management?

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Q. How does the CAP theorem impact the design of Big Data solutions?

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Q. What role does the concept of "eventual consistency" play in the CAP theorem?

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Q. What characterizes Big Data solutions in terms of data management aspects?

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Q. What is the trend in data structures for Big Data?

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Q. According to the CAP Theorem, what are the three main features that a distributed storage system can provide?

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