Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The enormous rate of increase in the data produced by various sources has resulted in the essential need for scalable, dependable, and efficient information processing frameworks. The key to such issues is high-performance data pipelines, particularly in areas where quick decision-making and real-time analytics are highly important. The article discusses how Apache Spark, Docker, and Kubernetes could be used to compose a strong and modern system to build such pipelines. Apache Spark offers good distributed processing functionality, Docker offers reliable and portable containerised environments, and Kubernetes offers this orchestration and scaling with auto-scaling. These technologies together can be used to make flexible, resilient high high-throughput data pipelines appropriate to both batch and streaming workloads. The article provides a detailed overview of how to build next-generation data infrastructures through the analysis of their respective functions and implementation, integration, real-world applications, as well as associated implementation issues. There are also best practices and performance considerations discussed, which aim at helping organizations in the implementation and sustenance of effective data pipeline solutions.