Zainudin Sidik, Abdul Rahman Ismail

Perbandingan Teknik Normalisasi dan Denormalisasi dalam Pengelolaan Data Skala Besar

Introduction

Perbandingan teknik normalisasi dan denormalisasi dalam pengelolaan data skala besar. Penelitian ini membandingkan normalisasi & denormalisasi dalam pengelolaan data skala besar. Temukan keunggulan performa, trade-off, dan strategi hybrid untuk optimasi big data.

Abstract

Penelitian ini bertujuan menganalisis perbandingan teknik normalisasi dan denormalisasi dalam pengelolaan data skala besar untuk mengidentifikasi karakteristik performa dan trade-offs yang terlibat. Metode penelitian menggunakan pendekatan eksperimental komparatif dengan mengimplementasikan dua sistem basis data yang identik namun berbeda dalam penerapan normalisasi dan denormalisasi, menggunakan dataset sintetis berukuran 500GB hingga 2TB untuk simulasi big data. Pengujian dilakukan dengan mengukur berbagai metrik performa meliputi waktu respons query, throughput system, utilisasi resource, dan scalability dalam berbagai skenario workload. Hasil penelitian menunjukkan denormalisasi memberikan keunggulan signifikan dalam operasi baca dengan peningkatan throughput 84,3% dan pengurangan waktu respons 73,8%, namun memerlukan konsumsi memori lebih besar 46% dibandingkan normalisasi. Evaluasi scalability menunjukkan denormalisasi mempertahankan performa konsisten pada beban tinggi, sementara normalisasi mengalami degradasi hingga 84% pada kondisi peak load. Normalisasi tetap unggul dalam integritas data dan efisiensi operasi tulis. Penelitian ini merekomendasikan penerapan strategi hybrid yang menggabungkan normalisasi untuk data transaksional dan denormalisasi selektif untuk data analitik, disesuaikan dengan karakteristik workload spesifik sistem untuk mencapai optimasi performa yang optimal.

Review

This study, "Perbandingan Teknik Normalisasi dan Denormalisasi dalam Pengelolaan Data Skala Besar," addresses a crucial topic in modern data management: the trade-offs between normalization and denormalization strategies when dealing with big data. The authors employ a comparative experimental approach, simulating big data environments with synthetic datasets ranging from 500GB to 2TB, to meticulously evaluate performance characteristics. The core contribution lies in quantifying the performance differentials, revealing that denormalization significantly enhances read operations and scalability under high load, albeit with increased memory consumption, while normalization maintains superior data integrity and write efficiency. A key practical recommendation emerging from this research is the adoption of a hybrid strategy, leveraging normalization for transactional data and selective denormalization for analytical workloads, tailored to specific system requirements. The methodological rigor of this research is commendable. By implementing two otherwise identical database systems differing only in their normalization strategy and subjecting them to various workload scenarios, the study provides a robust basis for comparison. The choice of large synthetic datasets (500GB to 2TB) appropriately simulates real-world big data challenges, and the comprehensive set of performance metrics—including query response time, system throughput, resource utilization, and scalability—ensures a holistic evaluation. The quantitative findings are particularly impactful, demonstrating denormalization's impressive 84.3% increase in throughput and 73.8% reduction in read response time, alongside its superior ability to maintain consistent performance under peak loads where normalization degrades by up to 84%. These specific metrics provide strong empirical evidence supporting the identified trade-offs. While the study provides valuable insights, future work could further explore the practical implementation nuances of the recommended hybrid strategy. Specifically, investigating various hybrid architectures, perhaps across different NoSQL or distributed SQL platforms, could expand the generalizability of these findings beyond traditional relational databases implied by the normalization/denormalization context. Additionally, examining the long-term maintenance overhead, potential data consistency challenges in complex hybrid systems, or the impact on query optimization complexity could offer a more complete picture. Nonetheless, this paper makes a significant contribution to the literature on big data management, offering clear, empirically-backed guidelines for architects and developers grappling with performance optimization in large-scale data environments. Its findings are highly relevant and will undoubtedly inform practical design decisions.

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