The rapid evolution of technology and the exponential growth of data have transformed numerous domains, allowing organizations. Modern products embedded with sensors and intelligent chips can track usage, system load, and environmental conditions, while emerging technologies enable early detection of component degradation and potential failures. Although big data has reshaped industries, its sheer volume, velocity, and complexity present significant challenges for maintaining reliable and resilient analytics pipelines, with optimization techniques playing a crucial role in strengthening system robustness. Data analytics has notably revolutionized maintenance practices, particularly predictive maintenance, which leverages machine learning, statistical modeling, and artificial intelligence to anticipate equipment failures. Likewise, fields such as reliability engineering, safety analytics, cybersecurity, and autonomous systems are increasingly applying machine learning to enhance performance and manage risks. Big data also underpins advancements in cloud computing, smart grids, and renewable energy, supporti
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