The advent of big data has revolutionized the field of data analytics, enabling organizations to extract valuable insights from vast and diverse datasets. However, the dynamic nature of big data workloads poses a significant challenge for traditional data processing systems, which often struggle to efficiently handle fluctuating query loads. To address this issue, the concept of elastic query processing has emerged as a crucial solution. This paper provides an in-depth exploration of auto-scaling solutions for big data analytics, highlighting the principles, techniques, and technologies that enable elastic query processing. Elastic query processing involves the automatic adjustment of computing resources to match the specific demands of incoming queries. This adaptation ensures optimal performance, resource utilization, and cost efficiency, making it a pivotal capability in today’s data-driven world. The paper investigates various aspects of elastic query processing, including the following key points: Challenges in Big Data Analytics, Auto-Scaling Strategies, Architectural Considerations, Key Technologies and Tools, Security and Data Governance, and Future Directions, This comprehensive examination of elastic query processing serves as a valuable resource for data engineers, architects, and decision-makers seeking to enhance their organization’s data processing capabilities. By understanding the principles and technologies underpinning auto-scaling solutions, businesses can adapt to the ever-evolving landscape of big data analytics, ensuring they harness the full potential of their data assets.
Keywords: Auto-Scaling Solutions, Big Data Analytics, Data Processing, Distributed Data Storage, Elastic Query Processing, Query Workload Management, Resource Optimization
