Citation :

Lalmohan Behera, Vishnu Vardhan Reddy Chilukoori, 2024. "End-to-End Data Pipelines: Redefining the Architecture of Data Engineering in Cloud Environments" ESP International Journal of Advancements in Science & Technology (ESP-IJAST) Volume 2, Issue 4: 26-33.

Abstract :

Data pipelines are the critical component of contemporary data engineering that assumes the responsibility of accessing, processing, and delivering data to various organisational applications. In cloud environments where high efficiency, scalable architecture, and flexibility are critical, the need for high-quality, reliable and efficient data pipeline architectures has increased. This paper looks at the various aspects of selecting an appropriate end-to-end data pipeline, emphasising architectural strategies compatible with scale, resilience, and real-time computation. Modern large-scale applications generate enormous amounts of data, and the solution is to employ cloud native technologies like Apache Kafka, Spark, etc., as well as cloud computing technologies like AWS Glue Google Dataflow to build reliable pipelines for different use cases. The work comprehensively reviews and analyzes innovative methods and practices and offers information about their effectiveness according to the application. Specific importance is paid to meta-information, which controls data flow to achieve minimum time between inputs and outputs, maximum dependability, and minimum costs. The guidelines for using adaptive cloud-based pipelines for real-time and batch processing are described through the empirical calculations of the metrics and the case studies presented in the paper. The following study will focus on providing practical findings and recommendations on how to reformulate the approach to building and managing data pipelines to achieve better data-to-insight times in cloud-dominated business landscapes.

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Keywords :

Data Pipelines, Cloud Computing, Data Engineering, Scalability, Real-time Processing, Fault Tolerance.