Scalable Data Pipeline Architecture for Real-Time Supply Chain Analytics Using PySpark and Snowflake
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I3P126Keywords:
PySpark, Snowflake, Data Pipeline, Supply Chain Analytics, Real-Time Processing, Data Engineering, Stream Processing, Analytical Architecture, Cloud Data Warehouse, Distributed ComputingAbstract
As the volume of data continues to grow in enterprises and the network is becoming increasingly complex, real-time supply chain analytics has emerged as a necessary tool for gaining greater visibility, responsiveness, and insight into the enterprise's operations. Historical data processing architectures have found themselves inadequately capable of managing the volume, velocity and variety of data coming into an enterprise via its supply chain from sources such as enterprise resource planning systems, warehouse management systems, transportation systems, iot devices and digital commerce systems. In this paper, a scalable data pipeline architecture for real-time supply chain analytics using Apache PySpark along with data pipeline component for distributed stream and batch data processing and Snowflake as a cloud-native analytical data warehouse is introduced. Data ingestion is designed to have scalable inbound data delivery rates; the data is ingested by a parallelized transformation flow; scalable storage; and low latency data analytical querying. That means the proposed architecture would enable near real-time operational intelligence. Technical and operational aspects of key architectural elements, such as Data Ingestion frameworks, Processing pipelines, orchestration mechanisms, and Analytical storage layers are explored. The performance evaluation shows that the architecture can process large scale data from supply chain efficiently while being scalable, reliable and cost-effective. The study also demonstrates some of the key considerations for actually implementing these, data governance frameworks, and optimization methods that allow companies to gain actionable insights from the perpetually changing supply chains. The proposed resolution has a modern, cloud-based solution for real-time supply chain analytics and has formed a base for further intelligent and AI-based source chain decision support systems.
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