Abstract

Data analytics pipelines are essential frameworks that consist of interrelated processes, beginning with data creation and ending with receipt and analysis. Pipelines enhance workflows by automating manual operations, maintaining smooth data flow, and enabling selection, extraction, transformation, validation, and visualisation activities. They demonstrate high efficiency by reducing errors and removing bottlenecks, enabling the simultaneous processing of several data streams. Data analytics pipelines are versatile, capable of handling batch and real-time processing and supporting various data inputs and destinations, such as visualisation and machine learning tools. Vigilant monitoring is required for the iterative process to ensure performance, validation, and fault identification, primarily due to obstacles such as data corruption and delay. As data sources increase and needs get more complicated, building, monitoring, and maintaining these pipelines becomes labour-intensive, frequently requiring human supervision. Data analytics pipelines are crucial for exploiting data in organisations, facilitating informed decision-making, and preserving a competitive advantage in today’s data-driven world. The proposed work addresses this challenge by developing a comprehensive end-to-end data pipeline. This unified solution integrates data processing, storage, and analysis to enable organisations to derive actionable insights from diverse data sources with exceptional efficiency and agility.