Big Data stream processing

Big Data stream processing

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At the beginning of twenty-first century, the research interest of a new model of streamlined data processing has been arising, involving a huge volume of data in today's market that makes it impossible to store and process data along with the traditional way. Data stream processing (DSP) is a data computational paradigm that enables the real-time processing of continuous data streams instead of maintaining the static relationship among them. In this model, a large volume of raw tuple of data enters in a rapid, continuous, and streaming manner to the ecosystem. Such a set of streams is unbounded in size, while the data arrival time and data processing time have an online nature.

Chapter Contents:

  • 7.1 Introduction to stream processing
  • 7.1.1 Background and motivation
  • 7.1.2 Streamlined data processing framework
  • 7.1.3 Stream processing systems
  • Aurora [1]
  • Yahoo S4 [3]
  • 7.2 Apache storm [8,9]
  • 7.2.1 Reading path
  • 7.2.2 Storm structure and composing components
  • 7.2.3 Data stream and topology
  • 7.2.4 Parallelism of topology
  • 7.2.5 Grouping strategies
  • Shuffle grouping
  • Fields grouping
  • Partial key grouping
  • All grouping
  • Global grouping
  • None grouping
  • Direct grouping
  • Local or shuffle grouping
  • 7.2.6 Reliable message processing
  • 7.3 Scheduling and resource allocation in Apache Storm
  • 7.3.1 Scheduling and resource allocation in cloud [4–7]
  • 7.3.2 Scheduling of Apache Storm [8,9]
  • 7.3.3 Advanced scheduling schemes for Storm
  • 7.4 Quality-of-service-aware scheduling
  • 7.4.1 Performance metrics [16]
  • 7.4.2 Model predictive control-based scheduling
  • 7.4.3 Experimental performance analysis
  • Experimental setting
  • Topology and workload attributes
  • Evaluation
  • 7.5 Open issues in stream processing
  • 7.6 Conclusion
  • Acknowledgement
  • References

Inspec keywords: Big Data; computer centres

Other keywords: data enters; data computational paradigm; continuous data stream real-time processing; Big Data stream processing; DSP; streamlined data processing

Subjects: Data handling techniques

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