Lake Data Warehouse Architecture for Big Data Solutions

Traditional Data Warehouse is a multidimensional repository. It is nonvolatile, ‎subject-oriented, integrated, time-variant, and non-‎operational data. It is gathered from multiple ‎heterogeneous data ‎sources. We need to adapt traditional Data Warehouse architecture to deal with the new ‎challenges...

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Bibliographic Details
Published inInternational journal of advanced computer science & applications Vol. 11; no. 8
Main Authors Saddad, Emad, El-Bastawissy, Ali, M., Hoda, Hazman, Maryam
Format Journal Article
LanguageEnglish
Published West Yorkshire Science and Information (SAI) Organization Limited 2020
Subjects
Agricultural research
Availability
Big Data
Computer architecture
Computer science
Data acquisition
Data analysis
Data warehouses
Decision making
Open source software
Relational data bases
Renewable resources
Validity
Visualization
Volatility
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Summary:Traditional Data Warehouse is a multidimensional repository. It is nonvolatile, ‎subject-oriented, integrated, time-variant, and non-‎operational data. It is gathered from multiple ‎heterogeneous data ‎sources. We need to adapt traditional Data Warehouse architecture to deal with the new ‎challenges imposed by the abundance of data and the current big data characteristics, containing ‎volume, value, variety, validity, volatility, visualization, variability, and venue. The new ‎architecture also needs to handle existing drawbacks, including availability, scalability, and ‎consequently query performance. This paper introduces a novel Data Warehouse architecture, named Lake ‎Data Warehouse Architecture, to provide the traditional Data Warehouse with the capabilities to ‎overcome the challenges. ‎Lake Data Warehouse Architecture depends on merging the traditional Data Warehouse architecture ‎with big data technologies, like the Hadoop framework and Apache Spark. It provides a hybrid ‎solution in a complementary way. The main advantage of the proposed architecture is that it ‎integrates the current features in ‎traditional Data Warehouses and big data features acquired ‎through integrating the ‎traditional Data Warehouse with Hadoop and Spark ecosystems. Furthermore, it is ‎tailored to handle a tremendous ‎volume of data while maintaining availability, reliability, and ‎scalability.‎
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2020.0110854