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Stream Data Model And Architecture In Big Data Pdf

stream data model and architecture in big data pdf

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Published: 15.04.2021

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.

Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation

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. A DSMS processes queries over a stream of data, by partitioning that stream in windows and evaluating the query for every new window, producing a never ending stream of results. The windows can be time-limited, size-limited, or punctuated by specific kinds of events.

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.

4 Key Components of a Streaming Data Architecture (with Examples)

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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. In all of these cases we have end devices that are continuously generating thousands or millions of records, forming a data stream — unstructured or semi-structured form, most commonly JSON or XML key-value pairs. A single streaming source will generate massive amounts of these events every minute. In its raw form, this data is very difficult to work with as the lack of schema and structure makes it difficult to query with SQL-based analytic tools; instead, data needs to be processed, parsed and structured before any serious analysis can be done.


MapReduce is a simple programming model that permits. processing a notions of big data, stream processing and the increasing. volume of.


stream data model and architecture pdf

A common use case that trips up those who are new to the concept is payment processing. Any number of processing modules can be pushed onto a stream. You bring the compute power to where the data resides. Modeling and managing data is a central focus of all big data projects. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements.

Top 15 Big Data Tools | Open Source Software for Data Analytics

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.

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5 Comments

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    15.04.2021 at 22:15
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    PDF | In this paper, we would like to discuss data stream processing in the big data area. Our goal is to provide a Stream-centric architecture on Apache Kafka [26] extensible data model that supports online analytic.

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