A Comparative Review of Data Warehousing ETL Tools with New Trends and Industry Insight

Mukherjee, Rajendrani; Kar, Pragma

doi:10.1109/iacc.2017.0192

Cited by 47 publications

(35 citation statements)

References 3 publications

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“…ETL is an acronym for extract, transform and load, and has been around for a quite a while in light of the fact that it is related with conventional batch processing in the data domain [9]. The purpose of data warehouses is to give businesses with data that they can solidify, analyze, and make cognizant thoughts out of that is pertinent to their focus/intents.…”

Section: B Etl Testingmentioning

confidence: 99%

Testing Big Data Applications

Punn¹,

Agarwal²,

Syafrullah³

et al. 2019

EECSI

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Today big data has become the basis of discussion for the organizations. The big task associated with big data stream is coping with its various challenges and performing the appropriate testing for the optimal analysis of the data which may benefit the processing of various activities, especially from a business perspective. Big data term follows the massive volume of data, (might be in units of petabytes or exabytes) exceeding the processing and analytical capacity of the conventional systems and thereby raising the need for analyzing and testing the big data before applications can be put into use. Testing such huge data coming from the various number of sources like the internet, smartphones, audios, videos, media, etc. is a challenge itself. The most favourable solution to test big data follows the automated/programmed approach. This paper outlines the big data characteristics, and various challenges associated with it followed by the approach, strategy, and proposed framework for testing big data applications.

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Section: B Etl Testingmentioning

confidence: 99%

Testing Big Data Applications

Punn¹,

Agarwal²,

Syafrullah³

et al. 2019

EECSI

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“…Data warehousing (DW) is a proper system which is used for reports and analysis and is widely considered an essential element for decision processes. In 1970s Bill Inmon was known as the father of DW and others (Barry Devlin, Ralph Kimball and Paul Murphy) who developed a theory of data warehousing with good examples [34]. The analysis of large quantity of data collected from different sources without powerful tools is of great challenge which has led to how data should be stored, integrate, managed and analysed.…”

Section: Overviewmentioning

confidence: 99%

“…The data warehouse integrates a wide range of heterogeneous data sources in multidimensional structures that support decision making [36]. Data warehousing applies ETL technology [36]; [34]. With respect to the integration process of different sources, this paper by [36] suggested the use of the contextual model to conceptual design (CMCD).…”

Section: Overviewmentioning

confidence: 99%

“…Exploration of a multidimensional model helps to identify the most significant dimensions for decision making. Any decision process requires data trends and predictions that are not immediately available in operational databases and also requires historical data whereas the operational database stores only current data and this will lead to challenges of missing information in the decision process [34].…”

Section: Overviewmentioning

confidence: 99%

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A Theoritical Exploration of Data Management and Integration in Organisation Sectors

Offia¹,

Crowe²

2019

IJDMS

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Big data development is a disturbing issue that will affect enterprise across various sectors. The increase of data volume, high speed of data generation and increasing rate of different data from heterogeneous sources have led to difficulties in data management. This paper first reviews different aspects of big data management, including data integration and traditional data warehouse, and their associated challenges. The problems include increase of redundant data, data accessibility, time consumption in data modelling and data movement from heterogeneous sources into a central database, especially in the big data environment. We then propose a logical data management approach using RESTview technology to integrate and analyse data, without fully adopting traditional ETL processes. Data that for governance, corporate, security or other restriction reasons cannot be copied or moved, can easily be accessed, integrated and analysed, without creating a central repository. Data can be kept in its original form and location, eliminating the movement of data, significantly speeding up the process and allowing for live data interrogation. It may not be the practical solution for every situation but, it is a feasible solution that is comparably cost effective.

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“…A proposition on how to extract data from Twitter based on keywords and store it in Hadoop Ecosystem [1] to carry out a sentiment analysis. This processing allows us to categorize tweets as positive, negative or neutral using scoring methods.…”

Section: Introductionmentioning

confidence: 99%

An Improved Social Media Analysis on 3 Layers: A Real Time Enhanced Recommendation System

Talhaoui¹,

Bour²,

Moulouki³

et al. 2018

ijacsa

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Abstract-The Internet can be considered as an open field for expression regarding products, politics, ideas, and people. Those expressive interactions generate a large amount of data pinned per users and groups. In that scope, Big data along with various technologies, such as social media, cloud computing, and machine learning can be used as a toolbox to make sense of the data and give the opportunity to generate efficient analysis and studies of the individuals and crowds regarding market orientation, politics, and industry. The recommendation system for this acts as the pillars of technology, in the field of sentiment analysis and predictive analysis to make sense of user's data. However, this complex operation comes at the price of this. To each analysis, there is a personalized architecture and tool. In this paper, a novel design of a recommender system is provided powered by sentiment analysis and predictive models applied onto an example of data flow from the social media Twitter.

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A Comparative Review of Data Warehousing ETL Tools with New Trends and Industry Insight

Cited by 47 publications

References 3 publications

Testing Big Data Applications

Testing Big Data Applications

A Theoritical Exploration of Data Management and Integration in Organisation Sectors

An Improved Social Media Analysis on 3 Layers: A Real Time Enhanced Recommendation System

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