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10. The Future of Big Data > Streaming Data Processing

Streaming Data Processing

Hadoop’s batch-oriented processing is sufficient for many use cases, especially where the frequency of data reporting doesn’t need to be up-to-the-minute. However, batch processing isn’t always adequate, particularly when serving online needs such as mobile and web clients, or markets with real-time changing conditions such as finance and advertising.

Over the next few years we’ll see the adoption of scalable frameworks and platforms for handling streaming, or near real-time, analysis and processing. In the same way that Hadoop has been borne out of large-scale web applications, these platforms will be driven by the needs of large-scale location-aware mobile, social and sensor use.

For some applications, there just isn’t enough storage in the world to store every piece of data your business might receive: at some point you need to make a decision to throw things away. Having streaming computation abilities enables you to analyze data or make decisions about discarding it without having to go through the store-compute loop of map/reduce.


  

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