Note of big data dummies:Looking at Real-Time and Non-Real-Time Requirements
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What is the impact when an organization can handle data that is streaming in real time? In general, this real-time approach is most relevant when the answer to a problem is time sensitive and business critical. This may be related to a threat to something important like detecting the performance of hospital equipment or anticipating a potential intrusion risk.
The following list shows examples of when a company wants to leverage this real-time data to gain a quick advantage:
- Monitoring for an exception with a new piece of information, like fraud/intelligence
- Monitoring news feeds and social media to determine events that may impact financial markets, such as a customer reaction to a new product announcement
- Changing your ad placement during a big sporting event based on realtime Twitter streams
- Providing a coupon to a customer based on what he bought at the point of sale
- Low latency: Latency is the amount of time lag that enables a service to execute in an environment. Some applications require less latency,which means that they need to respond in real time. A real-time stream is going to require low latency. So you need to be thinking about compute power as well as network constraints.
- Scalability: Scalability is the capability to sustain a certain level of performance even under increasing loads.
- Versatility: The system must support both structured and unstructured data streams.
- Native format: Use the data in its native form. Transformation takes time and money. The capability to use the idea of processing complex interactions in the data that trigger events may be transformational.
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- Note of big data dummies:Looking at Real-Time and Non-Real-Time Requirements
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- Note of big data dummies:Looking at Real-Time and Non-Real-Time Requirements
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