Cloud-Based Data Engineering for Scalable Business Analytics Solutions: Designing Scalable Cloud Architectures to Enhance the Efficiency of Big Data Analytics in Enterprise Settings

Abstrakti

The growing complexity of big data in enterprise environments necessitates scalable and efficient solutions for data analytics. This research focuses on designing and evaluating scalable cloud-based architecture tailored to address the challenges posed by vast and dynamic datasets. The objectives of the study include proposing a framework that integrates real-time and batch data pipelines, scalable storage, distributed processing, and advanced visualization tools. Using a case study approach, the framework was tested in diverse industries, including retail, finance, and healthcare, to evaluate its performance.

The methodology employs cloud-native solutions like Apache Kafka, Amazon S3, and Google BigQuery, combined with processing frameworks such as Apache Spark and Databricks. Results indicate significant improvements in processing speed, scalability, and cost efficiency compared to traditional systems. The findings demonstrate how cloud architecture enables enterprises to achieve real-time decision-making, optimize operations, and enhance overall agility.

Despite challenges such as vendor lock-in and data transfer costs, the study provides actionable recommendations for enterprises to leverage cloud-based data engineering effectively. Future directions explore advancements in serverless computing and edge analytics to further optimize performance and resource utilization. This research contributes to bridging the gap between big data demands and enterprise-level analytics capabilities.