Open AccessExperimental Analysis on Processing of Unbounded Data
Author(s) -
Nirav Bhatt,
Amit Thakkar
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i8158.078919
Subject(s) - computer science , batch processing , stream processing , timestamp , data processing , process (computing) , complex event processing , data processing system , data flow diagram , data stream , data mining , database , real time computing , distributed computing , programming language , telecommunications
Processing of unordered and unbounded data is the prime requirement of the current businesses. Large amount of rapidly generated data demands the processing of the same without the storage and as per the timestamp associated with it. It is difficult to process these unbounded data with batch engine as the existing batch systems suffer from the delay intrinsic by accumulating entire incoming records in a group prior to process it. However windowing can be useful when dealing with unbounded data which pieces up a dataset into fixed chunks for processing with repeated runs of batch engine. Contrast to batch processing, stream handling system aims to process information that is gathered in a little timeframe. In this way, stream data processing ought to be coordinated with the flow of data. In the real world the event time is always skewed with the processing time which introduce issues of delay and completeness in incoming stream of data. In this paper, we presented the analysis on the watermark and trigger approach which can be used to manage these unconventional desires in the processing of unbounded data.