Operational Effectiveness Using Cloud-Based ETL Pipelines on Large-Scale Data Platforms.

Authors

Varun Garg

Abstract

Processing big amounts of data across several sources in real-time depends critically on cloud-based ETL (Extract, Transform, Load) pipelines. Maintaining operational efficiency, meantime, when handling multi-source data intake creates major difficulties. These involve control of scalability, handling of data variance, low latency assurance, and error recovery automation. This work points out the primary difficulties keeping operating efficiency in cloud-based ETL pipelines and suggests solutions like using real-time processing systems and automation. We show how automatic scaling, resource optimization, and real-time mistake detection could boost the performance of contemporary tools such AWS Lambda, Apache Kafka, and Kinesis by means of analysis. We also assess how these methods guarantee continuous system uptime, increase throughput, and aid to lower operating bottlenecks. The results of this work help to maximize cloud-based ETL solutions for operational and economic effectiveness in big-scale data environments.

Keywords

Cloud-Based ETL, Operational Efficiency, Multi-Source Data Ingestion, Automation in ETL Pipelines, Real-Time Data Processing, Scalability, Resource Optimization, Error Detection and Recovery, Low Latency, Distributed Systems, AWS Lambda, Apache Kafka, AWS Kinesis, Big Data Analytics, Predictive Analytics, Data Variability, Edge Computing, Auto-Scaling, Proactive Monitoring, Fault Tolerance

Operational Effectiveness Using Cloud-Based ETL Pipelines on Large-Scale Data Platforms.. Varun Garg. 2021. IJIRCT, Volume 7, Issue 1. Pages 1-5. https://www.ijirct.org/viewPaper.php?paperId=2502086