Optimizing SQL Databases Forbig Data Workloads: Techniques and Best Practices

Abstract

In the era of big data, SQL databases face significant challenges in handling vast volumes of data efficiently. This article explores optimization techniques and best practices for enhancing the performance and scalability of SQL databases in handling big data workloads. The study addresses the significant challenges faced by traditional SQL databases, including scalability issues, performance bottlenecks, resource constraints, and data integration complexities. Through a comprehensive methodology involving literature review, case studies, expert interviews, and performance testing, the research identifies effective strategies such as indexing, partitioning, sharding, and caching. Findings from case studies in e-commerce and financial services sectors demonstrate substantial improvements in query performance and resource utilization, validating the practical benefits of these optimization techniques. The study underscores the importance of a multifaceted approach to database optimization, integrating both theoretical and practical insights to address the complexities of big data environments. By staying informed and adopting the latest optimization strategies, database administrators and IT professionals can ensure their SQL databases remain efficient, scalable, and capable of managing the increasing demands of large-scale data processing, ultimately enabling organizations to derive valuable insights from their data.

Big Data-Driven Decision Making in Project Management: A Comparative Analysis

“…Similarly, BIM models provide a digital representation of the physical and functional characteristics of a construction project, facilitating better collaboration among stakeholders and more efficient project planning and execution (Szafranko & Harasymiuk, 2022;Younus, Pathan, et al, 2024). Project management software equipped with advanced analytics capabilities allows for the integration of various data sources, providing a comprehensive view of project performance and enabling more effective control and management (Uzzaman et al, 2024). The literature also underscores the importance of data governance frameworks in ensuring the successful implementation of big data analytics in construction.…”

“…Predictive analytics, for instance, allows managers to forecast project timelines and costs with greater accuracy, helping to anticipate potential delays and budget overruns before they occur (Amin et al, 2024). Moreover, data-driven decision-making enhances strategic planning, enabling managers to develop more effective risk management strategies and contingency plans (Uzzaman et al, 2024). This shift from intuitive to evidence-based decision-making leads to better project outcomes and higher stakeholder satisfaction.…”

“…Despite its potential benefits, the adoption of big data analytics varies across different organizational contexts and project types, influenced by factors such as technological readiness, data integration complexities, and organizational culture (Amin et al, 2024). Addressing these challenges requires a concerted effort to build robust data governance frameworks, enhance digital literacy among project teams, and foster a culture that embraces data-driven decision-making (Uzzaman et al, 2024). As construction projects continue to grow in complexity and scale, the strategic use of big data analytics offers promising opportunities to improve efficiency, reduce costs, and deliver projects on time and within budget.…”