Study of Methods to Achieve Near Real Time ETL

Muddasir, N Mohammed; Raghuveer, K.

doi:10.1109/ctceec.2017.8455002

Cited by 6 publications

(1 citation statement)

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“…The (SSJA) algorithm supports highspeed streams with disk and memory access management. Muddasir and Raghuveer [15], discuss the three available techniques to achieve realtime ETL, including metadata management, change data capture (CDC) and distributed processing of Big Data. Meehanet al [2], propose a novel generic streaming ETL architecture for new real-time data warehouse applications.…”

Section: Related Workmentioning

confidence: 99%

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

Gueddoudj,

Chikh,

Attia

2023

ISI

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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.

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Section: Related Workmentioning

confidence: 99%