File Name: stream data model and architecture in big data .zip
A data stream is a real-time, continuous, ordered implicitly by arrival time of explicitly by timestamp sequence of items. It is impossible to control the order in which items arrive, nor it is feasible to locally store a stream in its entirety. Stream data management is important in telecommunications, real-time facilities monitoring, stock monitoring and trading, preventing fraudulent transactions and click stream analysis.
The growing amount of data in healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition. Based on the proposed architecture, a prototype implementation has been built for healthcare systems in order to generate real-time alerts. The suggested prototype is based on spark and MongoDB tools. In the face of such situation, relying on classical systems may result in a life quality decline for millions of people. Seeking to overcome this problem, a bunch of healthcare systems have been designed.
Today's market is flooded with an array of Big Data tools and technologies. They bring cost efficiency, better time management into the data analytical tasks. Here is the list of best big data tools and technologies with their key features and download links. This big data tools list includes handpicked tools and softwares for big data. It allows distributed processing of large data sets across clusters of computers. It is one of the best big data tools designed to scale up from single servers to thousands of machines. It delivers on a single platform, a single architecture and a single programming language for data processing.
The challenge of generating join results between two data streams is that, at any point of time, the view of the dataset is incomplete for both sides of the join making it much harder to find matches between inputs. Specialists It is common to address architecture in terms of specialized domains or technologies. Big data streaming is a process in which big data is quickly processed in order to extract real-time insights from it. In these lessons you will gain practical hands-on experience working with different forms of streaming data including weather data and twitter feeds. This dissertation proposes an architecture for cluster computing systems that can tackle emerging data processing workloads while coping with larger and larger scales.
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This article covers key concepts and design patterns for streaming data architecture. Streaming data is becoming a core component of enterprise data architecture due to the explosive growth of data from non-traditional sources such as IoT sensors, security logs and web applications. Streaming technologies are not new, but they have considerably matured in recent years. A streaming data source would typically consist of a stream of logs that record events as they happen — such as a user clicking on a link in a web page, or a sensor reporting the current temperature.
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