Os-ETL: A High-Efficiency, Open-Scala Solution for Integrating Heterogeneous Data in Large-Scale Data Warehousing

Abstract

The surge in data volume necessitates the integration of Resource Description Framework (RDF) data within corporate environments. While Extract, Transform, Load (ETL) processes exhibit proficiency with conventional data sources, their scalability diminishes when applied to large and highly varied data sources, inclusive of RDF data. The latter constitutes a wealth of knowledge that, when harnessed via data warehouse technology, can augment corporate value in a fiercely competitive milieu. The advent of platforms like polystore offers opportunities for advanced hardware deployment. ETL processes necessitate two crucial phases: Partitioning and data allocation. Concurrently, the scientific community is spurred to innovate ETL processes that support real-time analytics. This study proposes a novel architecture for ETL processes, named Open-Scala-ETL (Os-ETL). Equipped with a method for deploying a data warehouse based on a polystore, Os-ETL enables real-time analysis. The primary objective of the Os-ETL solution is to resolve the complexities in deploying a graph structure data warehouse on a polystore-a process that involves partitioning and data allocation. Os-ETL is a distributed solution that supports both batch and streaming processing using the Spark framework. Scala scripts are executed within this framework to partition RDF graphs and distribute the resultant fragments across various sites. The implementation of Os-ETL is based on Apache Spark, with ETL deployment on a Spark SQL polystore. This solution empowers companies with data warehouse technology to improve performance, scalability, and latency between a data warehouse and its data sources. The approach has been assessed and validated using largescale, heterogeneous data, encompassing the LUBM benchmark, CSV files, an Oracle database, and a Neo4j graph database. The results corroborate its superior performance in terms of scalability and optimization.

A Model for Enhancing Unstructured Big Data Warehouse Execution Time

“…The authors of [38] proposed Os-ETL, a novel ETL solution developed to combine heterogeneous data with large-scale data warehousing environments. This solution intends to increase the speed, flexibility, and performance of data integration operations.…”

Analysis of the Sentiment in the Evaluation Texts of University Students by Means of the Concept of Flexible Management

“…With the rapid development of information technology (IT) and the growing popularity of the Internet in today's society, big data has been widely used in all walks of life, including the education field [1][2][3]. In this context, it is easier to obtain the teaching evaluation text data of college students, which contains a large amount of teaching evaluation information, helping teachers provide timely feedback on the teaching effects and further optimizing teaching methods [4][5][6][7]. However, these teaching evaluation texts are diverse and complex, and they contain not only clear evaluation opinions but also a large number of implicit, vague, or emotionally rich descriptions.…”