CREATING EFFICIENT AND SCALABLE DATA PIPELINES FOR CLOUD-BASED ANALYTICS

Abstract

This article presents a comprehensive approach to optimizing data orchestration processes for cloud-based analytics by transitioning from Apache Airflow to Google Composer within the Google Cloud Platform (GCP) ecosystem. The research addresses critical challenges in data pipeline management, including security, efficiency, and scalability, while leveraging GCP's managed services and adhering to industry best practices. By implementing Google Dataproc for dynamic resource allocation and integrating the Data Build Tool (DBT) for streamlined analytics code deployment, the study demonstrates significant improvements in data processing capabilities. The new architecture, built on a secure Virtual Private Cloud (VPC) network, incorporates auto-scaling mechanisms for compute nodes, storage, and network resources, ensuring adaptability to varying workloads. Results show a 40% reduction in pipeline execution time, a 300% increase in data volume handling capacity, and a 25% reduction in cloud infrastructure costs. Furthermore, the integration of DBT facilitated rapid deployment of analytics code, fostering a collaborative development approach and adherence to software engineering best practices. This comprehensive solution not only enhanced data orchestration efficiency and pipeline scalability but also improved overall analytical capabilities, enabling more sophisticated machine learning models and reducing time-to-insight by 50%. The article provide valuable insights for organizations seeking to enhance their cloud-based analytics infrastructure in an increasingly data-driven business environment.